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How Can We Learn About Human Flourishing from Research?

We use techniques to deepen and broaden the social sciences.

Key points

  • Discerning truth about flourishing requires good data and the most rigorous methodologies.
  • Principles of causal inference, theoretically grounded measurement, and evidence synthesis are all important in discerning truth.
  • Integrating further insights from philosophy, theology, and history enriches our understanding of what truly contributes to well-being.

This post from the Human Flourishing Program at Harvard on methodology for flourishing is a bit more technical than others. Nonetheless, it seemed worthwhile to share a concise, synthetic account of our thinking to date about the best practices for investigating the constituents and causes of human flourishing. Employing the most rigorous methods not only enriches our understanding of flourishing, but is also crucial in ensuring the success of efforts to promote flourishing.

Methodology for Flourishing

To promote flourishing we must understand it. We must understand how it is distributed—who is flourishing and who is not and in what ways. We must also understand the factors that give rise to flourishing. Achieving such understanding is not necessarily an easy task. We need good data. We also need rigorous methodologies to help uncover the various determinants of flourishing. We thus seek to employ some of the most rigorous approaches to quantitative empirical analysis while also integrating this work with scholarship from the humanities. The program’s work has, in fact, helped pioneer new methodological approaches and some of these have arisen precisely from engagement with trying to study flourishing more comprehensively or from engagement with humanistic scholarship. The various approaches we have employed, and in some cases even developed, range from challenges in causal inference, to comprehensive analyses using outcome-wide studies or meta-analyses, to more integrative approaches to measurement and interfacing with the humanities.

Causal Inference

In our empirical work on the determinants of well-being, we try to employ, whenever possible, the most rigorous principles of causal inference, including employing longitudinal designs, rich confounder adjustment, controlling for baseline outcome and prior levels of exposure. We have published papers on study design considerations and confounding control principles summarizing the importance of these ideas. We also routinely use sensitivity analysis to assess how robust or sensitive our conclusions are to potential unmeasured confounding factors so as to better assess the strength of evidence. Some of our also work employs more sophisticated causal models such as marginal structural models and causal mediation analysis models to examine the effects of time-varying exposures or mechanisms. We have employed these approaches, for example, in our work on the effects of religious service attendance on mortality risk and depression and the potential mechanisms governing these relationships.

Minerva Studio/Adobe Spark
Source: Minerva Studio/Adobe Spark

Outcome-Wide Studies

Most quantitative empirical studies attempting to assess causality examine only a single exposure and a single outcome. However, this does not allow one to easily see how the exposure or phenomenon under study may affect other outcomes, or what the relative effect magnitudes are, or whether there may be harmful effects on certain outcomes and beneficial effects on others. A more comprehensive study of flourishing requires examining multiple outcomes simultaneously. The outcome-wide longitudinal design developed at the program extends classical approaches for causal inference to examine multiple outcomes simultaneously. We have published both brief and more comprehensive introductions to this analytic approach and have used this approach to examine the effects on a wide range of outcomes of numerous psychosocial phenomena and exposures including parental warmth, parenting practices, religious service attendance, religious upbringing, forgiveness, social cohesion, volunteering, hope, purpose in life, life satisfaction, character strengths, and financial conditions.

Meta-Analysis

The strongest evidence often comes from meta-analyses that combine evidence over multiple studies. We have developed new metrics for meta-analyses that better characterize evidence when effects may be heterogeneous across settings and may manifest potentially beneficial effects in some contexts and detrimental effects in others. We have also developed methods to help assess whether meta-analyses are robust to potential unmeasured confounding and to publication bias (wherein some studies end up not being published in the research literature and are thereby excluded from such meta-analyses). We have used these approaches to help try to resolve controversies in meta-analysis around the effects of violent video games and media exposure on suicide, smoking, and sexual behaviors, as well as to gain additional insight into supportive employment interventions and job-crafting practices at work.

Measurement

The assessment of psychosocial constructs is a perennial challenge in attempts to study well-being. A large psychometric literature has developed along with a host of empirical methodological tools. Unfortunately, most of the empirical approaches are based purely on correlations and ignore potential causal relations between the potential factors under study. Most of the literature on psychometric assessment also tends to ignore the rich insights and important distinctions that have arisen within philosophy and theology concerning the relevant constructs. We attempt to develop a more integrated theory of measurement, taking into account causal relationships and incorporating insights and analytic frameworks and definitions from the philosophical and theological literatures. Although this more integrated approach is still under development, a number of important critiques of existing practices have already emerged, including issues concerning causal relationships between factors under study, differential causal relationships between different indicators of the same construct, and causal interpretation of composite measures using scales or indices. We have also been working towards incorporating insights from the philosophical and theological literature to produce more conceptually satisfactory measures of meaning, suffering, and spiritual well-being, and to likewise inform current projects on hope, optimism, and love.

Humanistic Scholarship

The study of flourishing is inherently interdisciplinary, with various disciplines providing important and distinct insights. Although much of our research is empirical, we also contribute humanities scholarship on well-being, including work in philosophy, theology, and history. Moreover, we are working towards trying to better integrate or synthesize knowledge across disciplines so as to allow insights from one discipline to contribute to the pursuits of another. Such work has included developing empirical hypotheses based on philosophical and theological traditions; allowing claims in the humanities to potentially be challenged and informed by empirical research; incorporating philosophical and theological insights and distinctions into measure development; using philosophy and theology to enrich the interpretation of empirical results; employing empirical social science research methodologies to evaluate interventions based on philosophical or theological insights; and bringing insights together from different disciplines and synthesizing them. We have written a short working paper on some of these approaches. While much work remains to be done in developing more systematic approaches to the integration of knowledge across disciplines, we remain committed to an interdisciplinary approach to the study of human flourishing and to continuing to explore how various academic disciplines might better engage with one another.

We very much hope that the use of these various rigorous methodologies, and the integrating of insights from the humanities and social sciences, will help us better understand, and thereby also better promote, human flourishing.

Tyler J. VanderWeele, Director, Human Flourishing Program

References

Case, B.W. and VanderWeele, T.J. Integrating the humanities and the social sciences: six approaches and case studies. Harvard University Technical Report.

VanderWeele, T.J. (2021). Can sophisticated study designs with regression analyses of observational data provide causal inferences? JAMA Psychiatry, 78:244-246.

VanderWeele, T.J. (2019). Principles of confounder selection. European Journal of Epidemiology, 34:211-219.

VanderWeele, T.J., Mathur, M.B., and Chen, Y. (2020). Outcome-wide longitudinal designs for causal inference: a new template for empirical studies. Statistical Science, 35:437-466.

VanderWeele, T.J. (2017). Outcome-wide epidemiology. Epidemiology, 28:399-402.

VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167:268-274.

VanderWeele, T.J., Jackson, J.W., and Li, S. (2016). Causal inference and longitudinal data: a case study of religion and mental health. Social Psychiatry and Psychiatric Epidemiology, 51:1457-1466.

VanderWeele, T.J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. New York: Oxford University Press.

Mathur, M. and VanderWeele, T.J. (2020). Sensitivity analysis for unmeasured confounding in meta-analyses. Journal of the American Statistical Association, 115:163-172.

Mathur, M. and VanderWeele, T.J. (2019). New metrics for meta-analyses of heterogeneous effects. Statistics in Medicine, 3:1336-1342.

Mathur, M. and VanderWeele, T.J. (2020). Sensitivity analyses for publication bias in meta-analyses. Journal of the Royal Statistical Society, Series C, 69:1091-1119.

VanderWeele, T.J. (2022). Constructed measures and causal inference: towards a new model of measurement for psychosocial constructs. Epidemiology, 33:141-151.

VanderWeele, T.J. and Vansteelandt, S. (2022). A statistical test to reject the structural interpretation of a latent factor model. Harvard University Technical Report.

VanderWeele, T.J. and Batty, C.J.K. (2022). On the dimensional indeterminacy of one-wave factor analysis under causal effects. Harvard University Technical Report.

Mathur, M. and VanderWeele, T.J. (2020). Sensitivity analysis for unmeasured confounding in meta-analyses. Journal of the American Statistical Association, 115:163-172.

Mathur, M. and VanderWeele, T.J. (2019). New metrics for meta-analyses of heterogeneous effects. Statistics in Medicine, 3:1336-1342.

Mathur, M. and VanderWeele, T.J. (2020). Sensitivity analyses for publication bias in meta-analyses. Journal of the Royal Statistical Society, Series C, 69:1091-1119

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