For instance, economists conduct a supply/demand or incentives-based analysis of the field of economics itself to understand why they focus on some areas and not others or why the field has become more math-heavy over time.

Psychologists can also study the psychology of academic psychologists to understand the underlying psychological drivers that determine which areas of study are popular or why the replication crisis occurred (from the perspective of the psychology of those who precipitated and enabled it).

Sociologists may also apply ethnographic methods to examine the institutions, practices, and self-concepts of sociologists.

After all, what could be more available to study and more at the top of your mind than your own group? And why not apply your field’s methods to your group since you are already applying them to everything else?

But in my experience, this kind of “Wesearch” – if you’ll allow me to coin a term – is quite rare and niche.

If I’m right about this, why would that be? I suspect part of the reason is that people want to see themselves as not being merely governed by simple forces.

It’s all fine and good to model other anonymous people as merely responding to incentives, being impacted by severe confirmation bias, mimicking each other’s behavior for social status, etc. But we don’t want to think of ourselves, and the colleagues we respect in that way. It feels reductionist (and inaccurate) to do so. Our colleagues might even feel insulted to be modeled in such a way. These are models only for everyone else.

An interesting exception, pointed out by a reader, is that academic psychologists often have run studies on graduate students (e.g., their mental health and other psychological challenges they face). But even still, that’s only an example of it studying one aspect of itself.

This piece was first written on May 23, 2025, and first appeared on my website on May 26, 2025.

(1) In a large study we ran, IQ (measured by performance on intelligence tasks) had a strong correlation with self-reported (remembered) performance on the math portion of the SAT exam (r=0.61, n=714), which most participants would have taken MANY YEARS prior. This suggests that they were neither lying that often nor were their memories (of a long-ago event) that bad. Though I think there was likely some inflation in their self-reported scores, their reported scores contained a lot of signals.

(2) When we asked people how much they agreed with the extremely subjective statement “I feel satisfied with my income” (on a 7-point scale), their responses correlated quite strongly with their self-reported household income (r=0.43, n=639). This suggests that self-reported subjective feelings can map pretty well onto actual realities.

(3) When we asked, “Have you ever been diagnosed with depression by a medical or mental health professional?” it had a reasonable correlation with the level of agreement to the single statement, “I often tell myself that ‘I am not good enough’ (r=0.37, n=509). This suggests that agreement with vague, seeming statements can say a surprisingly large amount about a person.

(4) You might think that even if it’s an anonymous survey, people wouldn’t be willing to admit socially undesirable behaviors or traits, socially stigmatized events, or highly personal things. But, in our experience, many people are willing to indicate that these apply to them. In one study, when asked if they had ever cheated on a romantic partner, we found that 32% of respondents admitted to having done so.  In another, when asked whether, in childhood, an adult in their home ever hit, beat, kicked, or physically hurt them, 26% agreed. When asked if they had been raped, more than 20% agreed. While it’s certain that some people lie, many people are willing to honestly talk about their experiences in anonymous surveys.

While it’s important to be cautious about self-reporting on surveys – people can lie, they may not be paying attention, they may not remember, and they may lack self-insight – in my experience, it often works (perhaps surprisingly) well to tap important traits!

This piece was first written on March 29, 2025, and first appeared on my website on April 24, 2025.

Here’s my (relatively) easy method for getting a sense of what an academic discipline has been “thinking about” by quickly examining the top two most cited papers from five of the most influential journals in that discipline.

HOW TO FIGURE OUT WHAT’S INFLUENTIAL IN AN ACADEMIC FIELD

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Step 1: Go to the Google Scholar Top Publications tool (http://bit.ly/2EqfPrY). As a backup, you can alternatively use the Scimago Journal Rankings tool (http://bit.ly/2GVu79x), but it’s less convenient because it doesn’t link directly to papers. Choose both an academic research category (e.g., Social Sciences) and a subcategory (e.g., Archeology) that you’re interested in analyzing.

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Step 2: For the highest h5-index journal listed, right/control click the number in the “h5-index” column, and open up the sorted page of most cited articles from that journal in a new tab.

Note: the h5-index is a proxy for how influential or important a journal is in that field. If you’re curious, the h5-index is the h-index (http://bit.ly/2qcD0RH) limited to the last five years, and by definition, a scholar with an h-index of h “has published h papers each of which has been cited in other papers at least h times.” So it is kind of a weird, but somewhat reasonable measure, that takes into account both the number of publications and how many times they have been cited. In this case the h-index is being applied to the papers in an entire journal rather than for an individual academic. This is by no means a perfect method for measuring academic influence, and practitioners may dispute the exact rank ordering of journals, but hopefully what academics consider the top journals will typically rank near the top by this metric. An alternative possibility is to rank order by h5-median rather than the regular h5-index, which Google Scholar shows you but won’t sort by for you. According to http://bit.ly/2H8NlbJ, H5-median “is based on H5-index, but instead measures what the median (or middle) value of citations is for the h number of citations. A journal with an H5-index of 60 and H5-median of 75 means that, of those 60 articles with 60 or more citations, the median of those citation values is 75.”

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Step 3: In the new tab you’ve opened, click on the titles of the two most cited papers from that journal and read or save their abstracts.

Note: these papers will often be 3-7 years old. Presumably, this is because it takes a while for citations to accumulate, so the latest papers won’t have as many. If you want to focus on the most recent output of that field, pick a cutoff year, and as you scroll down through the most cited papers, skip any that didn’t come out after your cutoff.

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Step 4: Return to step 2 and repeat it for the next highest ranking h5-index journal listed for that field.

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Step 5: Keep going until you’ve read the abstracts from the two most cited papers from each of the five or so most influential journals in that field!

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Caveats: a limitation of the method is that what’s most cited in the “top” journals is not necessarily what people in that field consider most exciting or what is “hottest” right now. Plus, the specific method described above covers about the last 5 years, so will not be totally bleeding edge. Also, the “subcategories” of fields can be broader than you’d ideally want (e.g. “philosophy” is a subcategory according to the procedure above). And there can be many different important themes at a time, and this method certainly won’t capture them all. Finally, it’s worth noting that this approach counts citations coming from outside of the field as well as ones from inside the field (making it not a pure way of analyzing what’s popular IN the field).

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Here’s what we get if we apply this technique to “philosophy” (below) and to “gender studies” (below that). I did not use a cutoff year (I picked the papers with the most citations, regardless of year).

My take away from this philosophy list (below) is that one trend in academic philosophy appears to be a focus inwards, asking questions like: “Which methods of philosophical inquiry are valid?”, “what do philosophers believe?” and “What is the scope of philosophy?”, etc.

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The top 2 most cited papers from each of the top 5 highest h5-index philosophy journals according to Google Scholar:

1. The theory of judgment aggregation: an introductory review

https://link.springer.com/article/10.1007/s11229-011-0025-3

This paper provides an introductory review of the theory of judgment aggregation. It introduces the paradoxes of majority voting that originally motivated the field, explains several key results on the impossibility of propositionwise judgment aggregation, presents a pedagogical proof of one of those results, discusses escape routes from the impossibility and relates judgment aggregation to some other salient aggregation problems, such as preference aggregation, abstract aggregation and probability aggregation. The present illustrative rather than exhaustive review is intended to give readers who are new to the field of judgment aggregation a sense of this rapidly growing research area.

2. Algebraic foundations for the semantic treatment of inquisitive content

https://link.springer.com/article/10.1007/s11229-013-0282-4

In classical logic, the proposition expressed by a sentence is construed as a set of possible worlds, capturing the informative content of the sentence. However, sentences in natural language are not only used to provide information, but also to request information. Thus, natural language semantics requires a logical framework whose notion of meaning does not only embody informative content, but also inquisitive content. This paper develops the algebraic foundations for such a framework. We argue that propositions, in order to embody both informative and inquisitive content in a satisfactory way, should be defined as non-empty, downward closed sets of possibilities, where each possibility in turn is a set of possible worlds. We define a natural entailment order over such propositions, capturing when one proposition is at least as informative and inquisitive as another, and we show that this entailment order gives rise to a complete Heyting algebra, with meet, join, and relative pseudo-complement operators…

3. What do philosophers believe?

https://link.springer.com/article/10.1007/s11098-013-0259-7

What are the philosophical views of contemporary professional philosophers? We surveyed many professional philosophers in order to help determine their views on 30 central philosophical issues. This article documents the results. It also reveals correlations among philosophical views and between these views and factors such as age, gender, and nationality. A factor analysis suggests that an individual’s views on these issues factor into a few underlying components that predict much of the variation in those views. The results of a meta survey also suggest that many of the results of the survey are surprising: philosophers as a whole have quite inaccurate beliefs about the distribution of philosophical views in the profession.

4. Metaphysics as modeling: the handmaiden’s tale

https://link.springer.com/article/10.1007/s11098-012-9906-7

Critics of contemporary metaphysics argue that it attempts to do the hard work of science from the ease of the armchair. Physics, not metaphysics, tells us about the fundamental facts of the world, and empirical psychology is best placed to reveal the content of our concepts about the world. Exploring and understanding the world through metaphysical reflection is obsolete. In this paper, I will show why this critique of metaphysics fails, arguing that metaphysical methods used to make claims about the world are similar to scientific methods used to make claims about the world, but that the subjects of metaphysics are not the subjects of science. Those who argue that metaphysics uses a problematic methodology to make claims about subjects better covered by natural science get the situation exactly the wrong way around: metaphysics has a distinctive subject matter, not a distinctive methodology. The questions metaphysicians address are different from those of scientists, but the methods employed to develop and select theories are similar…

5. Cognitive Penetration of Colour Experience: Rethinking the Issue in Light of an Indirect Mechanism

https://onlinelibrary.wiley.com/…/…/j.1933-1592.2010.00481.x

Can the phenomenal character of perceptual experience be altered by the state of one’s cognitive system, for example, one’s thoughts or beliefs? If one thinks this can happen (at least in certain ways that are identified in the paper) then one thinks that there can be cognitive penetration of perceptual experience; otherwise, one thinks that perceptual experience is cognitively impenetrable…

6. Intuitions and Experiments: A Defense of the Case Method in Epistemology

https://onlinelibrary.wiley.com/…/…/j.1933-1592.2012.00634.x

Contemporary epistemologists employ various methods in the course of articulating and defending their theories. A method that has attracted particular scrutiny in recent years involves the production of intuitive responses to particular cases: epistemologists describe a person making some judgment and then invite their audience to check this judgment’s epistemic status for themselves.

7. Cognitive Penetrability and Perceptual Justification

https://onlinelibrary.wiley.com/…/…/j.1468-0068.2010.00786.x

It is sometimes said that in depression, everything looks grey. If this is true, then mood can influence the character of perceptual experience; depending only on whether a viewer is depressed or not, how a scene looks to that viewer can differ even if all other conditions stay the same. This would be an example of cognitive penetration of visual experience by another mental state…

8. Slurring Words

https://onlinelibrary.wiley.com/…/…/j.1468-0068.2010.00820.x

Increasingly philosophers (and linguists) are turning their attention to slurs—a lexical category not much explored in the past…

9. The cultural ecosystem of human cognition

https://www.tandfonline.com/…/…/10.1080/09515089.2013.830548

Everybody knows that humans are cultural animals. Although this fact is universally acknowledged, many opportunities to exploit it are overlooked. In this article, I propose shifting our attention from local examples of extended mind to the cultural-cognitive ecosystems within which human cognition is embedded. I conclude by offering a set of conjectures about the features of cultural-cognitive ecosystems.

10. Moral intuitions: Are philosophers experts?

https://www.tandfonline.com/…/…/10.1080/09515089.2012.696327

Psychologists and experimental philosophers have reported findings showing that in some cases ordinary people’s moral intuitions are affected by factors of dubious relevance to the truth of the intuition. Some defend the use of intuition as evidence in ethics by arguing that philosophers are the experts in this area, and philosophers’ moral intuitions are both different from those of ordinary people and more reliable. We conducted two experiments indicating that philosophers and non-philosophers do indeed sometimes have different moral intuitions, but challenging the notion that philosophers have better or more reliable intuitions.

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The top 2 most cited papers from each of the top 5 highest h5-index gender studies journals according to Google Scholar:

1. The Role of Parents and Teachers in the Development of Gender-Related Math Attitudes

https://link.springer.com/article/10.1007/s11199-011-9996-2

Girls tend to have more negative math attitudes, including gender stereotypes, anxieties, and self-concepts, than boys. These attitudes play a critical role in math performance, math course-taking, and the pursuit of math-related career paths. We review existing research, primarily from U.S. samples, showing that parents’ and teachers’ expectancies for children’s math competence are often gender-biased and can influence children’s math attitudes and performance. We then propose three new directions for future research on the social transmission of gender-related math attitudes. First, parents’ and teachers’ own math anxieties and their beliefs about whether math ability is a stable trait may prove to be significant influences on children’s math attitudes. Second, a developmental perspective that investigates math attitudes at younger ages and in relation to other aspects of gender development, such as gender rigidity, may yield new insights into the development of math attitudes. Third, investigating the specific behaviors and mannerisms that form the causal links between parents’ and teachers’ beliefs and children’s math attitudes may lead to effective interventions to improve children’s math attitudes from a young age. Such work will not only further our understanding of the relations between attitudes and performance, but will lead to the development of practical interventions for the home and classroom that ensure that all students are provided with opportunities to excel in math.

2. The Role of Stereotype Threats in Undermining Girls’ and Women’s Performance and Interest in STEM Fields

https://link.springer.com/article/10.1007/s11199-011-0051-0

In the present manuscript we draw on the Multi-Threat Framework to explore gender-related math attitudes and how they put girls and women at risk for stereotype threats. Gunderson et al. (2011) detail how negative stereotypes about women’s math abilities are transmitted to girls by their parents and teachers, shaping girls’ math attitudes and ultimately undermining performance and interest in science, technology, engineering, and math (STEM) fields. The social-psychological phenomenon of stereotype threat complements this approach and demonstrates the additional ways in which gender-related math attitudes undermine girls’ and women’s interest and performance in STEM domains. Considering the phenomenon of stereotype threat also identifies how stereotypes and other gender-related math attitudes can undermine women’s and girls’ interest and performance in STEM domains even when women and girls have positive math attitudes.

3. Extensive Mothering: Employed Mothers’ Constructions of the Good Mother

http://journals.sagepub.com/doi/abs/10.1177/0891243211427700

Social scientists have provided rich descriptions of the ascendant cultural ideologies surrounding motherhood and paid work. In this article, I use in-depth interviews with a diverse sample of 40 employed mothers to explore how they navigate the “intensive mother” and “ideal worker” ideologies and construct their own accounts of good mothering. Married mothers in this sample construct scripts of “extensive mothering,” in which they delegate substantial amounts of the day-to-day child care to others, and reframe good mothering as being “in charge” of and ultimately responsible for their children’s well-being. Single mothers describe extensive mothering in different ways, and their narratives suggest less accountability to the “intensive mothering” model. Mothers in this sample also justify employment in novel ways: They emphasize the benefits of employment for themselves—not only their children—and they reject the long work hours imposed by an ideal worker model. The article ends with the implications of extensive mothering for the motherhood and employment literatures and for gender equality.

4. Gendered Organizations in the New Economy

http://journals.sagepub.com/doi/abs/10.1177/0891243212445466

Gender scholars draw on the “theory of gendered organizations” to explain persistent gender inequality in the workplace. This theory argues that gender inequality is built into work organizations in which jobs are characterized by long-term security, standardized career ladders and job descriptions, and management controlled evaluations. Over the past few decades, this basic organizational logic has been transformed. In the so-called new economy, work is increasingly characterized by job insecurity, teamwork, career maps, and networking. Using a case study of geoscientists in the oil and gas industry, we apply a gender lens to this evolving organization of work. This article extends Acker’s theory of gendered organizations by identifying the mechanisms that reproduce gender inequality in the twenty-first-century workplace, and by suggesting appropriate policy approaches to remedy these disparities.

5. Hard-won and easily lost: A review and synthesis of theory and research on precarious manhood.

http://psycnet.apa.org/record/2012-24955-001

This article reviews evidence that manhood is seen as a precarious social status that is both difficult to achieve and tenuously held. Compared with womanhood, which is typically viewed as resulting from a natural, permanent, and biological developmental transition, manhood must be earned and maintained through publicly verifiable actions. Because of this, men experience more anxiety over their gender status than women do, particularly when gender status is uncertain or challenged. This can motivate a variety of risky and maladaptive behaviors, as well as the avoidance of behaviors that might otherwise prove adaptive and beneficial. We review research on the implications of men’s precarious gender status across the domains of risk-taking, aggression, stress and mental health, and work-life balance. We further consider how work on precarious manhood differs from, and can add to, work on individual differences in men’s gender role conflict. In summary, the precarious manhood hypothesis can integrate and explain a wide range of male behaviors and phenomena related to the male gender role. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

6. Real men don’t eat (vegetable) quiche: Masculinity and the justification of meat consumption.

http://psycnet.apa.org/record/2012-30417-001

As arguments become more pronounced that meat consumption harms the environment, public health, and animals, meat-eaters should experience increased pressure to justify their behavior. Results of a first study showed that male undergraduates used direct strategies to justify eating meat, including endorsing pro-meat attitudes, denying animal suffering, believing that animals are lower in a hierarchy than humans and that it is human fate to eat animals, and providing religious and health justifications for eating animals. Female undergraduates used the more indirect strategies of dissociating animals from food and avoiding thinking about the treatment of animals. A second study found that the use of these male strategies was related to masculinity. In the two studies, male justification strategies were correlated with greater meat consumption, whereas endorsement of female justification strategies was correlated with less meat and more vegetarian consumption. These findings are among the first to empirically verify Adams’s (1990) theory on the sexual politics of meat linking feminism and vegetarianism. They suggest that to simply make an informational appeal about the benefits of a vegetarian diet may ignore a primary reason why men eat meat: It makes them feel like real men. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

7. Slaying the Seven‐Headed Dragon: The Quest for Gender Change in Academia

https://onlinelibrary.wiley.com/…/…/j.1468-0432.2011.00566.x

In this article, we propose a multi‐level distinction between gender inequality practices and gender equality practices to come to a better understanding of the slow pace of gender change in academia. Gender inequality resembles an unbeatable seven‐headed dragon that has a multitude of faces in different social contexts. Based on an empirical study on the recruitment and selection of full professors in three academic fields in The Netherlands, we discuss practices that should bring about gender equality and show how these interact with gender inequality practices. We argue that the multitude of gender inequality practices is insufficiently countered by gender equality practices, because the latter lack teeth, especially in traditional masculine academic environments.

8. Women and Top Leadership Positions: Towards an Institutional Analysis

https://onlinelibrary.wiley.com/doi/full/10.1111/gwao.12018

Women remain under‐represented in top leadership positions in work organizations, a reality that reflects a variety of barriers that create a glass ceiling effect. However, some women do attain top leadership positions, leading scholars to probe under what conditions women are promoted despite seemingly intractable and well‐documented barriers. Previous scholarship tends to posit individual‐level explanations, suggesting either that women who attain top leadership positions are exceptional or that potential women leaders lack key qualities, such as assertiveness. Much less scholarship has explored institutional‐level mechanisms that may increase women’s ascension to top positions. This analysis seeks to fill this gap by testing three institutional‐level theories that may shape women’s access to and tenure in top positions: the glass cliff, decision‐maker diversity, and the savior effect. To test these theories, we rely on a dataset that includes all CEO transitions in Fortune 500 companies over a 20‐year period. Contrary to the predictions of the glass cliff, we find that diversity among decision-makers — not firm performance — significantly increases women’s likelihood of being promoted to top leadership positions. We also find, contrary to the predictions of the savior effect, that diversity among decision-makers increases women leaders’ tenure as CEOs regardless of firm performance. By identifying contextual factors that increase women’s mobility, the paper makes an important contribution to the processes that shape and reproduce gender inequality in work organizations.

9. The Role of Self-Objectification in Disordered Eating, Depressed Mood, and Sexual Functioning Among Women

http://journals.sagepub.com/doi/abs/10.1177/0361684311420250

Our study aimed to offer a comprehensive test of the model outlined in the objectification theory (Fredrickson & Roberts, 1997). A sample of 116 Australian female undergraduate students completed measures of self-objectification, self-surveillance, body shame, appearance anxiety, internal body awareness, flow, disordered eating, depressed mood, and sexual functioning. Simple correlations showed that most variables were related as predicted. Structural equation modeling showed an acceptable level of fit of the data to the theoretical model. Nevertheless, predictive ability was considerably greater for disordered eating than for depressed mood, which in turn was greater than for sexual functioning. Appearance anxiety and body shame emerged as the major mediating variables. The findings provide strong evidence in support of the objectification theory. In particular, we concluded that self-objectification plays an important role in the development of mental health issues in young women. Accordingly, intervention strategies that target either societal objectification practices themselves, or educate young women to resist the pressures inherent in these practices that lead to self-objectification, have potentially far-reaching benefits.

10. An Intersectional Analysis of Gender and Ethnic Stereotypes: Testing Three Hypotheses

http://journals.sagepub.com/doi/abs/10.1177/0361684312464203

We compared perceived cultural stereotypes of diverse groups varying by gender and ethnicity. Using a free-response procedure, we asked 627 U.S. undergraduates to generate ten attributes for 1 of 17 groups: Asian Americans, Blacks, Latinos, Middle Eastern Americans, or Whites; men or women; or ten gender-by-ethnic groups (e.g., Black men or Latina women). Based on intersectionality theory and social dominance theory, we developed and tested three hypotheses. First, consistent with the intersectionality hypothesis, gender-by-ethnic stereotypes contained unique elements that were not the result of adding gender stereotypes to ethnic stereotypes. Second, in support of an ethnicity hypothesis, stereotypes of ethnic groups were generally more similar to stereotypes of the men than of the women in each group. Third, a gender hypothesis postulated that stereotypes of men and women would be most similar to stereotypes of White men and White women, less similar to ethnic minority men and ethnic minority women, and least similar to Black men and Black women. This hypothesis was confirmed for target women, but results for target men were mixed. Collectively, our results contribute to research, theory, and practice by demonstrating that ethnic and gender stereotypes are complex and that the intersections of these social categories produce meaningful differences in the way groups are perceived.

Below are 166 (rather interesting!) size-ordered correlations that I calculated on 870 people in the United States, who were recruited using our study recruitment platform, Positly.

All responses are self-reported by the study participants, mostly measured on a scale of 1-4 or 1-5, except those that suggest a different scale (e.g., number of minutes doing something, age, symptom scores, etc.)

Keep in mind that if A and B are correlated, it could be that A causes B, it could be that B causes A, or it could be that some third thing causes both A and B.

The number shown on each row is the correlation between the thing on the left of the “vs.” and the thing on the right of the “vs.”

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Huge correlations (~67% of variance explained)

0.82: depression symptom score (PHQ9) vs. anxiety symptom score (GAD7)
0.82: “Taking all things together, I am happy.” (1-5 scale) vs. “In general, I feel confident and positive about myself.” (1-5 scale)
0.75: “How religious do you consider yourself to be?” (0-5 scale) vs. “How spiritual do you consider yourself to be?” (0-5 scale)
0.75: satisfactionWithDatingOrRomanticLife vs. howWellTreatedByRomanticPartner
0.74: socialLiberalness vs. economicLiberalness
0.73: placeOfWorshipAffiliation vs. “How religious do you consider yourself to be?” (0-5 scale)
0.68: “In general, I feel confident and positive about myself.” (1-5 scale) vs. “How optimistic a person are you usually?” (-2 to 2 scale)
0.67: parentsHappyWhenYouWereGrowingUp vs. parentsTreatedWellWhenGrowingUp
0.67: videoGameMinutesPerDayWhenPlays vs. videoGamePlayingDaysPerWeek
0.60: howGoodLifeIsRelativeToExpectations vs. “I feel satisfied with what I am achieving in life.” (1-5 scale)
0.60: enoughMoneyToLiveComfortably vs. moreWealthyThanFriends
0.60: hoursWorkedOnWorkDays vs. daysWorkedPerWeek
0.58: takesAntidepressants vs. hasMentalHealthDiagnosis
0.57: satisfactionWithDatingOrRomanticLife vs. relationshipSeriousness
0.54: takesAntidepressants vs. seeingAMentalHealthProfessional
0.54: “How spiritual do you consider yourself to be?” (0-5 scale) vs. placeOfWorshipAffiliation
0.53: alcoholDaysDrinkingPerWeek vs. drinksPerTimeDrinking

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Large correlations (~25% of variance explained)

0.50: relationshipSeriousness vs. livesWithOtherPeople
0.49: conscientiousness (2-question big 5 scale) vs. howGoodIsWillpower
0.48: “Taking all things together, I am happy.” (1-5 scale) vs. satisfactionWithDatingOrRomanticLife
0.48: hasDepressiveDisorder vs. takesAntidepressants
0.48: enoughMoneyToLiveComfortably vs. howGoodLifeIsRelativeToExpectations
0.46: relationshipSeriousness vs. hasChildren
0.45: hasDepressiveDisorder vs. hasAnxietyDisorder
0.42: selfReportedHowGoodAtMathComparedToOthers vs. selfReportedIntelligence
0.41: howWellTreatedByRomanticPartner vs. “Taking all things together, I am happy.” (1-5 scale)
0.40: “In general, I feel confident and positive about myself.” (1-5 scale) vs. howGoodIsWillpower
0.40: incomeScore vs. enoughMoneyToLiveComfortably
0.38: age vs. hasBeenDivorced
0.38: howOftenReadsBlogs vs. howOftenReadsTheNews
0.38 : timesSexualActivityWithOtherPersonIn7Days vs. howWellTreatedByRomanticPartner
0.37: moreWealthyThanFriends vs. “I feel satisfied with what I am achieving in life.” (1-5 scale)
0.36: howGoodLifeIsRelativeToExpectations vs. howWellTreatedByRomanticPartner
0.36: hasChildren vs. age
0.36: “Taking all things together, I am happy.” (1-5 scale) vs. enoughMoneyToLiveComfortably
0.36: selfReportedIntelligence vs. selfReportedGoodLookingnessForAge
0.36: selfReportedGoodLookingnessForAge vs. “In general, I feel confident and positive about myself.” (1-5 scale)
0.35 : timesSexualActivityWithOtherPersonIn7Days vs. satisfactionWithDatingOrRomanticLife
0.35: parentsHappyWhenYouWereGrowingUp vs. “In general, I feel confident and positive about myself.” (1-5 scale)
0.34: selfReportedIntelligence vs. considersSelfLifelongLearner
0.34: exerciseDaysPerWeek vs. hoursOutsideWeekly
0.31: homeOwnershipLevel vs. age
0.31: seeingAMentalHealthProfessional vs. depression symptom score (PHQ9)

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Medium correlations (~9% of variance explained)

0.3: “Taking all things together, I am happy.” (1-5 scale) vs. numberOfCloseFriendsAndFamilyMembers
0.3: homeOwnershipLevel vs. enoughMoneyToLiveComfortably
0.3: depression symptom score (PHQ9) vs. takesAntidepressants
0.3: republican1DemocratNegative1 vs. “How religious do you consider yourself to be?” (0-5 scale)
0.29: earlyTechnologyAdopter vs. appsTheyDownloadedAndUseWeekly
0.28: restednessUponWakingAfterIdealHoursOfSleep vs. “I feel satisfied with what I am achieving in life.” (1-5 scale)
0.28: healthLevel vs. exerciseDaysPerWeek
0.28: healthLevel vs. moreWealthyThanFriends
0.27: educationScore vs. incomeScore
0.27: selfReportedGoodLookingnessForAge vs. moreWealthyThanFriends
0.27 : isChristian vs. republican1DemocratNegative1
0.27: hasAPet vs. isFemale
0.26: selfReportedIntelligence vs. selfReportedGoodnessAndMoralness
0.25: numberOfCloseFriendsAndFamilyMembers vs. “How optimistic a person are you usually?” (-2 to 2 scale)
0.24: “How spiritual do you consider yourself to be?” (0-5 scale) vs. age
0.23: age squared vs. “How spiritual do you consider yourself to be?” (0-5 scale)
0.23: smokeCigarettesFrequency vs. addictedToADrugOtherThanCaffeineOrNicotine
0.23: hoursWorkedOnWorkDays vs. positivenessOfFeelingsAboutJob
0.23: hasAPet vs. isWhite
0.22: selfReportedIntelligence vs. educationScore
0.22: anxiety symptom score (GAD7) vs. takesAntidepressants
0.22: depression symptom score (PHQ9) vs. debtsCannotPayLevel
0.22: “How optimistic a person are you usually?” (-2 to 2 scale) vs. parentsTreatedWellWhenGrowingUp
0.21: hoursPastMidnightGoesToSleep vs. videoGameMinutesPerDayWhenPlays
0.21: hoursOutsideWeekly vs. exerciseMinutesPerExerciseDay
0.21: satisfactionWithDatingOrRomanticLife vs. parentsHappyWhenYouWereGrowingUp
0.21: socialLiberalness vs. homosexuality
0.21 : isChristian vs. hasChildren

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Small correlations (~4% of variance explained)

0.2: howOftenReadsTheNews vs. considersSelfLifelongLearner
0.2: hoursPastMidnightGoesToSleep vs. depression symptom score (PHQ9)
0.2: earlyTechnologyAdopter vs. internetCapableSmartphone
0.2: howGoodIsWillpower vs. moreWealthyThanFriends
0.2: nightclubAttendance vs. timesSexualActivityWithOtherPersonIn7Days
0.2: daysPerWeekUsesTwitter vs. appsTheyDownloadedAndUseWeekly
0.2: “How spiritual do you consider yourself to be?” (0-5 scale) vs. republican1DemocratNegative1
0.2: enoughMoneyToLiveComfortably vs. positivenessOfFeelingsAboutJob
0.2: bookReadingDaysPerWeek vs. considersSelfLifelongLearner
0.19: daysPerWeekUsesFacebook vs. isFemale
0.19: bodyMassIndex vs. depression symptom score (PHQ9)
0.19: differenceBetweenAverageAndNeededSleepHours vs. “Taking all things together, I am happy.” (1-5 scale)
0.19: selfReportedGoodLookingnessForAge vs. exerciseMinutesPerExerciseDay
0.19: selfReportedIntelligence vs. earlyTechnologyAdopter
0.18: hasAPet vs. daysPerWeekUsesFacebook
0.18: howWellTreatedByRomanticPartner vs. parentsTreatedWellWhenGrowingUp
0.18: “How spiritual do you consider yourself to be?” (0-5 scale) vs. “How optimistic a person are you usually?” (-2 to 2 scale)
0.18: moreWealthyThanFriends vs. timesSexualActivityWithOtherPersonIn7Days
0.18: nightclubAttendance vs. drinksPerTimeDrinking
0.18: incomeScore vs. selfReportedGoodLookingnessForAge
0.17 : firedFromAJobInLast12Months vs. addictedToADrugOtherThanCaffeineOrNicotine
0.17: isFemale vs. “How spiritual do you consider yourself to be?” (0-5 scale)
0.17: numberOfCloseFriendsAndFamilyMembers vs. “How religious do you consider yourself to be?” (0-5 scale)
0.17: selfReportedHowGoodAtMathComparedToOthers vs. selfReportedGoodLookingnessForAge
0.17: educationScore vs. enoughMoneyToLiveComfortably
0.17: selfReportedIntelligence vs. moreWealthyThanFriends
0.17 : timesSexualActivityWithOtherPersonIn7Days vs. healthLevel
0.17: daysPerWeekSpeaksToFriends vs. numberOfCloseFriendsAndFamilyMembers
0.17: “How religious do you consider yourself to be?” (0-5 scale) vs. takingAllThingsTogetherIAmHAPPY
0.16: minutesOnComputerTypicalDay vs. hoursPastMidnightGoesToSleep
0.16: hasBeenDivorced vs. “How spiritual do you consider yourself to be?” (0-5 scale)
0.16 : timesSexualActivityWithOtherPersonIn7Days vs. selfReportedGoodLookingnessForAge
0.16: hoursOutsideWeekly vs. “How optimistic a person are you usually?” (-2 to 2 scale)
0.16: “How spiritual do you consider yourself to be?” (0-5 scale) vs. numberOfCloseFriendsAndFamilyMembers
0.16: educationScore vs. bookReadingDaysPerWeek
0.16: howGoodIsWillpower vs. positivenessOfFeelingsAboutJob
0.16: gotDivorcedInLastTwoYears vs. addictedToADrugOtherThanCaffeineOrNicotine
0.16: howOftenReadsTheNews vs. educationScore
0.16: hasChildren vs. hasAPet
0.16: differenceBetweenAverageAndNeededSleepHours vs. exerciseDaysPerWeek
0.16: bodyMassIndex vs. chronicMedicalConditionSeriousness

—
Tiny correlation (~2% of variance explained)

0.15: anxiety symptom score (GAD7) vs. socialLiberalness
0.15: restednessUponWakingAfterIdealHoursOfSleep vs. positivenessOfFeelingsAboutJob
0.15: relationshipSeriousness vs. republican1DemocratNegative1
0.15: daysPerWeekSpeaksToFriends vs. howOftenReadsTheNews
0.15: alcoholDaysDrinkingPerWeek vs. smokeCigarettesFrequency
0.15: gotDivorcedInLastTwoYears vs. firedFromAJobInLast12Months
0.15: selfReportedGoodLookingnessForAge vs. satisfactionWithDatingOrRomanticLife
0.15: videoGameMinutesPerDayWhenPlays vs. bookReadingMinutesPerDayWhenRead
0.14: parentsHappyWhenYouWereGrowingUp vs. selfReportedGoodLookingnessForAge
0.14: isChristian vs. daysPerWeekUsesFacebook
0.14: considersSelfLifelongLearner vs. howOftenReadsBlogs
0.14: healthLevel vs. differenceBetweenAverageAndNeededSleepHours
0.14 : republican1DemocratNegative1 vs. homeOwnershipLevel
0.13: hasAPet vs. mentalIllnessInFamilyLevel
0.13: economicLiberalness vs. daysPerWeekUsesTwitter
0.13: economicLiberalness vs. hoursPastMidnightGoesToSleep
0.13: seeingAMentalHealthProfessional vs. gotDivorcedInLastTwoYears
0.13: videoGamePlayingDaysPerWeek vs. bodyMassIndex
0.13: “How spiritual do you consider yourself to be?” (0-5 scale) vs. physicalDisabilityLevel
0.12: bornInUS vs. hasAPet
0.12: howGoodIsWillpower vs. numberOfCloseFriendsAndFamilyMembers
0.12: howOftenReadsBlogs vs. educationScore
0.12: “How optimistic a person are you usually?” (-2 to 2 scale) vs. hasBeenDivorced
0.12: parentsTreatedWellWhenGrowingUp vs. “How religious do you consider yourself to be?” (0-5 scale)
0.12: urbannessOfLocation vs. economicLiberalness
0.12: isFemale vs. depression symptom score (PHQ9)
0.11: nightclubAttendance vs. selfReportedGoodLookingnessForAge
0.11: howOftenReadsTheNews vs. daysPerWeekUsesTwitter
0.11: worksMoreThanWouldLike vs. depression symptom score (PHQ9)
0.11: incomeScore vs. hasAPet
0.11: parentsHappyWhenYouWereGrowingUp vs. howGoodIsWillpower
0.11: isFemale vs. hasMentalHealthDiagnosis
0.11: hasAnxietyDisorder vs. hasAPet
0.11: usCitizen vs. isWhite
0.11: howOftenReadsBlogs vs. socialLiberalness
0.11 : firedFromAJobInLast12Months vs. nightclubAttendance
0.11: educationScore vs. appsTheyDownloadedAndUseWeekly

—
Negligible correlations (~1% of variance explained)

0.10: videoGameMinutesPerDayWhenPlays vs. hasMentalHealthDiagnosis
0.09: Points (gad7) vs. smokeCigarettesFrequency
0.09: healthLevel vs. alcoholDaysDrinkingPerWeek
0.09: howOftenReadsTheNews vs. incomeScore
0.08: selfReportedHowGoodAtMathComparedToOthers vs. howOftenReadsTheNews
0.07: physicalDisabilityLevel vs. lookingForWork
0.07: isMidwestUSRegion vs. hasChildren
0.07: gotDivorcedInLastTwoY

This piece was first written on January 13, 2018, and first appeared on my website on July 30, 2025.

Many causes of a population’s height distribution are not hereditary. For example, a population’s height is also determined by economic factors, like whether there was malnourishment among children or pregnant mothers. Cultural causes may also be at play, such as the extent to which height was valued in mate selection or whether the population has a gender imbalance. Even smaller cultural elements, like whether it is common for children to be enrolled in grueling, height halting athletics, whether height altering surgery is used to satisfy aesthetic preferences, and whether growth hormone is administered to children, can have an effect. Height is also influenced by long term trends, such as the percentage of people in each age group.

So, to what extent is height genetic? There is no single answer to that question. It depends on what you mean, and it may be that none of the answers satisfy what you think you mean by the question.

You could ask the extent to which one individual person’s height (as measured at one particular point in time) was “caused” by genetics. Genetics did cause their height, in the sense that, if you swapped some of their genes, they would have had a different height. Their height was effected by their nutrition as an infant (e.g., protein, vitamin A, vitamin D, and calcium). If they had better or worse nutrition, their height could have been different. Their height was also caused by their age since if you’d measured their height when they were one year old, they would have been a lot shorter.

Suppose you have an apple tree that has seven apples. What caused it to have seven apples as opposed to fewer or more? Was it the amount of sunlight it got? The amount of water? The average temperature? The soil it was planted in? This question doesn’t make sense to ask. The number of apples that this tree has is a complex function of all of these variables (plus many more), and even if we knew this complex function, at best all we could say (by taking derivatives with respect to one variable at a time) is how the number of apples would have changed with a small change in one of the inputs (holding the others constant), but even that isn’t the question we are attempting to ask.

Okay, perhaps it is more meaningful to talk about groups rather than individuals. We can ask, within a particular group, the extent that height is genetically determined.

The first problem we encounter is that the answer may depend a lot on the group we pick. If we are talking about a group of males and females who are all the same age and who all received the same nutrition when young, then we’ll find that a larger percentage of the variation in height is explainable from genetics than if we’re talking about a group of males of varying ages and varying early nutrition. The more variation that happens to have been caused by non-genetic factors like nutrition, the less heritable we’ll find the trait to be.

For instance, according to Scientific American [1], a study of Finnish twins found the heritability of height to be 78% for men and 75% for women. Another study found the heritability of height in China to be 65%. Hence, it technically makes no sense to say, “How much of height is determined by genetics? (full stop)” We have to pick a group G and then ask, “How much of height for group G is determined by genetics?” We’ll get a different answer for Finland than we will for China, and we’ll get a different answer for Finland in 2017 than we will for Finland in 1990. The distribution of genes changes over time in a population, as do the strength of environmental impacts, which, of course, also change the extent to which traits seem to be caused by genetics.

Okay, so suppose we’ve picked a group G. How then would we calculate how heritable height is within that group G? Well, ideally, we’d look at identical twins within G that were separated at birth and raised by different families. Because the age of these twins is identical and the genetic factors are close to identical (save for chance mutations and differeing gene expression), if the environmental factors are selected purely at random (with likelihood representative of how those factors affect members of G), we can assume that differences in the height of identical twins are mostly caused by non-genetic factors. Hence, we can look at the ratio of the amount height varies among twins raised in different families (the environmental component) to the amount that height varies among unrelated people raised in different families (which includes both genetic and environmental components). A similar sort of analysis can be done by comparing identical twins to fraternal twins.

Unfortunately, even these sorts of analyses make a variety of assumptions. They assume that the hormonal environment of the womb (which is shared by identical twins but which may not be mainly genetic) has no effect on height. They assume that the families that give birth to identical twins and the families that adopt them are representative of the broader population (e.g., if twins are adopted only by families that can provide for their children proper amounts of nutrition whereas many other families don’t, then the calculations will be skewed). And they assume that there are no hidden non-linear interactions between genetics and environment (for instance, if some genes cause a person’s height to be more sensitive to early nutrition, we may falsely chalk up any such height differences purely to nutrition differences when genetics are partly responsible). There is also, of course, sampling error. A large number of twins is hard to come by, and if we only manage to get 100 of them, the error in our heritability estimates could be +- 10% from just noise alone, not taking into account the other potential issues.

While the measurement of height is quite straightforward, for other variables of interest, we may find different heritability depending on the exact definition of the trait used. For instance, this remarkable chart [3] showing happiness heritability estimates that range from 18%-47% (mean=32%) for life satisfaction measures and 23%-59% (mean=36%) for wellbeing measures. The wide range of values occurs in part because of the variability in which measures of happiness were used [4], but also because of the different populations studied, different techniques used to make the estimates, and ordinary sampling error [5].

There are enough challenges with computing heritability that, at least occasionally, when the assumptions of the models are accidentally violated, the results of such analyses are hilariously non-sensical [2]: “‘Did you have your back rubbed’ came out to 92% [heritable] for males and 21% for females. Many of the resulting [narrow heritability estimates] exceed 100%. For example, the HRs of ‘Did you wear sunglasses after dark?’ Are 130% for males and 103% for females.”

What is perhaps most pernicious about calculations of heritability, though, is that people assume a high level of heritability implies a trait cannot be changed through environmental interventions, and more generally, people assume that the proportion that is heritable cannot be changed. You hear this, for instance, when people investigate the heritability of happiness and assume that the heritable portion is fixed, a predestined part of our fate that we societally have no control of.

The idea that the proportion attributed to genetics is unchangeable is false. For instance, you may have a group where CURRENTLY nearly all of the variation in height is determined by genetics, but height could still be dramatically changed via interventions such as better childhood nutrition or growth hormone. Heritability only tells us about the fraction of the CURRENT variation that seems to be due to environmental differences, not the fraction that COULD BE if we were to change the environment.

[1] https://www.scientificamerican.com/…/how-much-of-human…

[2] On Models and Muddles of Heritability https://link.springer.com/…/10.1023%2FA%3A1018358504373…

[3] https://www.ncbi.nlm.nih.gov/…/PMC4346667/figure/Fig2/

[4] https://www.ncbi.nlm.nih.gov/…/PMC4346667/table/Tab1/

[5] Genetics of Wellbeing and Its Components Satisfaction with Life, Happiness, and Quality of Life – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4346667/