Social Sciences

Abstract

The social sciences are the disciplines that study human beings as social creatures — how they think, associate, govern, trade, and make meaning together. In MeSH's classification they sit among the behavioral sciences, alongside psychology, and they share a defining ambition: to explain human conduct with the rigor of a science while studying a subject that talks back, varies by culture, and changes when it is measured. This article traces that ambition from Comte's positivism through Weber's interpretive turn, examines how the fields borrowed the machinery of measurement and causal inference, and confronts the credibility crisis that forced them to re-examine their own findings. The social sciences are best understood not as failed natural sciences but as a distinct enterprise, disciplined by evidence yet honest about the reflexivity of studying ourselves.

Keywords: social sciences, positivism, replication crisis

What the Social Sciences Are

The social sciences are the family of disciplines that study human beings in their social aspect: the regularities of how people associate, cooperate, compete, govern, exchange, and give their lives shared meaning. They include sociology, anthropology, economics, political science, demography, and — at the seam with the natural sciences — psychology and linguistics. MeSH files the group among the behavioral sciences, the branch of science concerned with the actions and reactions of organisms, so the social sciences are, in that classification, the part of behavioral science whose unit of analysis is the group, the institution, and the society rather than the individual organism alone.

What unites the disciplines is less a shared method than a shared difficulty. Their subject is reflexive: the things they study — people, and the collectivities people form — have beliefs about themselves, respond to being studied, and differ across cultures and eras in ways that atoms and cells do not. The founding question of the social sciences, and the one every generation has re-answered, is whether a science of so mutable and self-aware a subject is possible at all, and if so, what kind of science it can be. The history that follows is the history of two answers — that human conduct can be explained by the same causal logic as nature, and that it can only be understood from the inside — and of the long attempt to have both.

Types of Social Sciences

The Medical Subject Headings thesaurus places Social Sciences directly beneath Behavioral Sciences and enumerates a set of narrower descriptors beneath it. These children are an indexing classification, not a theory of the field: MeSH exists to tag the biomedical literature consistently, so its subdivisions mix academic disciplines (anthropology, sociology, economics) with objects those disciplines study (government, politics, quality of life) and cut across one another rather than partitioning the field cleanly. Read as a map of what the social sciences touch, however, the list is instructive; the linked entries below are those with a dedicated article on this site.

Note. The MeSH children of Social Sciences are an indexing vocabulary, not a taxonomy of academic disciplines: the categories overlap and mix fields with their objects of study. Only descriptors with a dedicated route are linked.

The disciplines differ less in subject than in their unit of analysis — the level at which each cuts into social life. Select one to see what it studies, how it studies it, and a founding figure.

Note. The units overlap — a sociologist and a psychologist can both study prejudice — but each field privileges a different level of explanation. That division of labor, and the friction where the levels meet, is much of what makes the social sciences a family rather than a single science.

From Positivism to Interpretation

The social sciences were born with a program, and its author was Auguste Comte, who coined the word sociology and proposed positivism: the doctrine that society can be studied by the same observational, law-seeking methods that had made the natural sciences triumphant. Comte imagined a hierarchy of sciences culminating in a science of society, and Émile Durkheim gave that ambition a working method. In The Rules of Sociological Method (1895), Durkheim insisted that social facts — ways of acting and thinking that exist outside any individual and constrain them — be treated as things, explained not by the psychology of individuals but by other social facts (#ref-durkheim-1982). His study of suicide, showing that rates varied lawfully with social integration rather than private despair, was the demonstration that a genuinely social cause could be isolated and measured.

Against this stood a second tradition, and its architect was Max Weber. Weber accepted that social science should seek causal explanation, but argued that human action differs from natural events in one decisive respect: it carries meaning. To explain why a person acts, one must grasp the subjective significance the act holds for them — a mode of understanding Weber called verstehen. Social science, on this view, is doubly bound: it must be interpretive, reconstructing the meanings actors attach to their conduct, and yet it must also strive to be value-free, keeping the researcher's own moral commitments out of the analysis even while studying a value-laden subject. The tension between Durkheim's externalist, fact-seeking science and Weber's meaning-centered one is not a historical curiosity; it is the permanent fault line of the social sciences, resurfacing in every debate between quantitative and qualitative method. Later philosophers sharpened the stakes: Karl Popper argued that the mark of a genuine science is falsifiability and warned, in The Poverty of Historicism (1957), against grand theories of historical destiny that no evidence could refute (#ref-popper-1957), while Thomas Kuhn's The Structure of Scientific Revolutions (1962) countered that even the natural sciences advance through paradigms — shared frameworks that shape what counts as a fact — a picture that gave the interpretive social sciences a way to see their own contested foundations as normal rather than deficient (#ref-kuhn-1962).

Measurement and Validity

If the social sciences were to be sciences, they had to measure things — attitudes, intelligence, prejudice, status — that cannot be seen directly. This is the problem of construct validity, and its classic statement is Lee Cronbach and Paul Meehl's 1955 paper: when a test claims to measure a theoretical construct that has no single observable criterion, its validity must be established by embedding the construct in a nomological network of predicted relationships and showing the measure behaves as the theory says it should (#ref-cronbach-1955). A measure of anxiety is valid not because it matches some gold standard — there is none — but because it correlates with what anxiety should correlate with and diverges from what it should not. Measurement in the social sciences is thus inseparable from theory: one cannot validate the instrument without committing to a theory of the thing measured.

Measuring is only half the task; the other half is inferring cause from data gathered in a world that cannot always be experimented on. Donald Campbell, with Julian Stanley, gave the social sciences their framework for this in Experimental and Quasi-Experimental Designs for Research (1963), distinguishing internal validity — whether the observed effect was really produced by the presumed cause — from external validity — whether it generalizes beyond the study — and cataloguing the quasi-experimental designs that approximate a true experiment when random assignment is impossible (#ref-campbell-1963). The framework let researchers reason explicitly about the threats — history, maturation, selection, regression to the mean — that a correlation might mask. Yet Paul Meehl, revisiting the field decades later, issued a famous warning: in the soft areas of the social sciences, theories are rarely tested by risky predictions that could fail, and the usual ritual of rejecting a null hypothesis of zero effect proves almost nothing, because in a richly connected social world everything correlates with everything to some degree (#ref-meehl-1978). The measurement machinery was sophisticated; the inferential logic it served was often weak.

A social scientist rarely measures a construct perfectly. Spearman’s correction shows that the correlation you observe is the true correlation multiplied by the square root of the two measures’ reliabilities:r_observed = r_true × √(rel_X × rel_Y)Drag the sliders to see how noisy instruments hide a real relationship.

Note. With two measures of reliability 0.80, even a substantial true correlation of 0.50 is observed as just 0.40 — a fifth of the signal lost to noise alone. This is why construct validity and reliable measurement are not housekeeping but the precondition for detecting any effect at all.

The Credibility Revolution

Meehl's warning proved prophetic. Two developments in the twenty-first century forced the social sciences to confront how much of their published knowledge would survive scrutiny. The first was a challenge to who had been studied. Joseph Henrich, Steven Heine, and Ara Norenzayan showed that the experimental base of behavioral science rests overwhelmingly on WEIRD subjects — Western, Educated, Industrialized, Rich, and Democratic — who are, on many psychological measures, among the least representative humans on Earth, so that claims about “human nature” drawn from undergraduate samples may not generalize even to the rest of humanity (#ref-henrich-2010). The second was a challenge to whether the findings held at all. The Open Science Collaboration's mass effort to repeat 100 published psychology studies found that only a minority produced significant results the second time, and effect sizes shrank by roughly half — the empirical opening of what became known as the replication crisis (#ref-osc-2015).

The diagnosis pointed to the exact weakness Meehl had named. When effects are studied at low statistical power, when researchers enjoy undisclosed flexibility in analysis, and when journals publish only positive results, a large fraction of statistically significant findings will be false positives even though every one cleared the p < .05 bar. Brian Nosek and colleagues formalized the response, distinguishing reproducibility (getting the same result from the same data), replicability (getting it from new data), and robustness (getting it under different analytic choices), and building the infrastructure — preregistration, registered reports, open data — meant to raise all three (#ref-nosek-2022). The reform was painful but clarifying: it recast the credibility of a finding not as a property it has once published, but as something established only by its survival across independent tests. The worked example below shows why so many "significant" results were destined to fail.

Among 1,000 hypotheses, only a fraction are actually true. Studies detect true effects at the rate of their power and flag false ones at the rate α = 0.05. The share of significant results that are real — the positive predictive value — can be shockingly low.

Note. Set π = 0.10 and power = 0.40 — realistic for much of the social sciences — and the PPV falls to about 0.47: most published “discoveries” would be false even though every one cleared p < .05. Raising power and studying more plausible hypotheses is the only way out, which is exactly what the credibility reforms target.

Worked Example

Why can a field publish thousands of statistically significant results and still find that most do not replicate? The arithmetic is a base-rate problem, laid out in John Ioannidis's much-cited argument that under realistic conditions most published research findings are false (#ref-ioannidis-2005). Suppose that in some research area only a minority of the hypotheses investigated are actually true — say the prior probability that any given hypothesis is true is π = 0.10. Suppose studies are run at power = 0.40 (a realistic figure for the social sciences, where large samples are costly) and use the conventional false-positive rate α = 0.05. Set the demo above to these values to follow along.

Among 1,000 hypotheses tested, 100 are true and 900 false. The true ones yield significant results at the rate of the power: 0.40 × 100 = 40 true positives. The false ones yield significant results at the rate of α: 0.05 × 900 = 45 false positives. So the number of significant findings is 40 + 45 = 85, of which only 40 are real.

The positive predictive value — the probability that a significant result reflects a true effect — is therefore PPV = 40 / 85 = 0.47. Fewer than half of the published “discoveries” in this scenario are true, even though every one of them reached p < .05. The false-discovery rate is 45 / 85 ≈ 53%. Nothing here involves fraud or incompetence; it follows purely from testing mostly-false hypotheses at modest power. Raise the power to 0.80 and the true positives climb to 80 while the false positives stay at 45, lifting the PPV to 80 / 125 = 0.64; raise the base rate of true hypotheses as well and it climbs further. This is the engine behind the replication crisis, and the reason the reforms target power, publication bias, and analytic flexibility rather than any single bad study.

Discussion

The recurring temptation in thinking about the social sciences is to grade them against physics and find them wanting — less precise, less cumulative, less able to predict. The comparison mistakes the nature of the enterprise. The social sciences study a subject that is reflexive: it holds beliefs, responds to being described, and changes as its own knowledge of itself grows. A physical law does not care whether it is known; an economic forecast can move the market it forecasts, and a sociological category can reshape the people it names. This does not exempt the fields from rigor — Durkheim's social facts and Campbell's validity threats are as demanding as any laboratory protocol — but it means their achievements look different: robust regularities that hold within a context rather than universal constants, and understanding of why people act rather than only prediction of what they will do.

Seen this way, the credibility crisis is not evidence that the social sciences failed but evidence that they took their own scientific commitments seriously enough to audit them. Few fields have subjected their published corpus to systematic replication; that psychology did so, and reformed its methods in response, is a mark of scientific maturity rather than decay (#ref-nosek-2022). The WEIRD critique similarly turned a blind spot into a research program on human variation (#ref-henrich-2010). The Gulbenkian Commission, chaired by Immanuel Wallerstein, argued in Open the Social Sciences (1996) that the nineteenth-century division into separate disciplines was itself a historical artifact due for rethinking, and the movement of the last two decades — toward shared standards of evidence, open data, and cross-disciplinary problems — is arguably that rethinking underway (#ref-wallerstein-1996). The social sciences remain a distinct kind of knowledge: disciplined by evidence, permanently self-aware, and unwilling to pretend that studying ourselves is the same as studying the stars.

Current Directions

The most active frontier is computational social science, the study of human behavior through the digital traces people now leave at population scale. David Lazer and a large group of colleagues announced the field in 2009, arguing that email, mobile phones, and online networks had made it possible to observe social life with a granularity and completeness the classic survey could never match (#ref-lazer-2009). A decade later the same authors took stock more soberly, cataloguing the obstacles — access to proprietary data, privacy, reproducibility, and the ethics of studying people through platforms they do not control — that stand between the promise and a durable science (#ref-lazer-2020). Duncan Watts, one of the field's founders, has pressed two demands on it: that social science become genuinely cumulative rather than a collection of one-off findings (#ref-watts-2007), and that it be more solution-oriented, organized around the practical problems it could help solve rather than disciplinary tradition alone (#ref-watts-2017).

The frontier has also delivered a sharp test of the field's ambitions. Matthew Salganik and colleagues ran the Fragile Families Challenge, a mass collaboration in which hundreds of researchers used a rich longitudinal dataset and modern machine learning to predict life outcomes — a child's future GPA, material hardship, eviction — from thousands of variables measured earlier. The result was humbling: even the best models predicted these outcomes only weakly, barely improving on a handful of simple benchmarks (#ref-salganik-2020). The finding cuts against the assumption that more data and better algorithms will inevitably make human lives predictable, and it returns the social sciences to their founding question in a new key: the limits on predicting individual trajectories may be a deep feature of social life, not a temporary shortage of data. Computational methods have expanded what the social sciences can see; they have not dissolved the reflexive, context-bound character that made these disciplines distinctive in the first place.

Common Misconceptions

The social sciences are just common sense dressed up in jargon.

Systematic study routinely overturns common sense: Durkheim showed suicide rates follow social integration rather than individual mood, and survey data repeatedly contradict confident folk intuitions about how people behave (#ref-durkheim-1982).

They are not real sciences because they cannot run experiments.

Much of social science is experimental, and where randomized experiments are impossible, quasi-experimental designs allow disciplined causal inference — Campbell and Stanley built an entire framework for exactly this (#ref-campbell-1963).

The replication crisis means the findings are all worthless.

It means the fields tested their own corpus and found that low power, publication bias, and analytic flexibility inflate false positives — a diagnosis that identifies which findings to trust and how to do better, not a verdict that none can be trusted (#ref-osc-2015).

Findings about people apply to everyone equally.

Most behavioral research has drawn on WEIRD samples that are unrepresentative even of humanity as a whole, so generalizing from them to all people is exactly the inference the evidence cautions against (#ref-henrich-2010).

Glossary

Behavioral sciences.

The branch of science concerned with the actions and reactions of organisms; the parent kind under which MeSH files the social sciences.

Computational social science.

The study of human and social behavior through large-scale digital trace data and computational methods, emerging in the 2000s.

Construct validity.

The degree to which a test measures the theoretical construct it claims to, established by fitting the measure into a network of predicted relationships rather than to a single criterion.

External validity.

The extent to which a study's findings generalize beyond its particular sample, setting, and conditions.

Internal validity.

The extent to which an observed effect can be attributed to the presumed cause rather than to confounding influences.

Paradigm.

In Kuhn's sense, a shared framework of assumptions, methods, and exemplary problems that guides a scientific community and defines what counts as a fact.

Positive predictive value (PPV).

The probability that a statistically significant finding reflects a true effect; determined jointly by statistical power, the false-positive rate, and the prior probability that the hypothesis is true.

Positivism.

Comte's doctrine that society can be studied by the observational, law-seeking methods of the natural sciences, yielding objective knowledge of social regularities.

Quasi-experiment.

A study that estimates a causal effect without full random assignment, using design features to rule out threats to internal validity.

Replication crisis.

The finding, prominent from the 2010s, that a large share of published results in psychology and adjacent fields fail to reproduce when the studies are repeated.

Social fact.

Durkheim's term for a way of acting or thinking that exists outside the individual, exerts constraint, and must be explained by other social facts rather than by individual psychology.

Statistical power.

The probability that a study will detect an effect of a given size when the effect is real; low power inflates the share of significant results that are false.

Verstehen.

Weber's method of interpretive understanding — grasping the subjective meaning an actor attaches to conduct as part of explaining it.

WEIRD samples.

Research participants who are Western, Educated, Industrialized, Rich, and Democratic — the overrepresented and atypical base of much behavioral research.

Key Researchers

Donald T. Campbell (1916-1996). American methodologist who, with Julian Stanley, formalized quasi-experimental design and the theory of internal and external validity that social scientists still use to reason about causal inference outside the laboratory. Wikipedia - Wikidata

Auguste Comte (1798-1857). French philosopher who coined the term sociology and founded positivism, the doctrine that the social world can be studied by the methods of natural science. Wikipedia - Wikidata

Émile Durkheim (1858-1917). A founder of modern sociology who established its empirical method, treating social facts as things to be explained by other social facts. Wikipedia - Wikidata

David Lazer (living). Political and computational social scientist at Northeastern University; lead author of the 2009 and 2020 manifestos that defined computational social science as a field. ORCID - Google Scholar - Faculty Page - Wikipedia

Brian A. Nosek (living). Psychologist and metascientist at the University of Virginia who co-founded the Center for Open Science and led the Reproducibility Project, forcing the social sciences to confront the credibility of their own findings. ORCID - Google Scholar - Faculty Page - Wikipedia

Matthew J. Salganik (living). Sociologist of the digital age at Princeton University who led the Fragile Families Challenge, a mass collaboration that measured the limits of predicting life outcomes from data. Google Scholar - Faculty Page - Wikipedia

Duncan J. Watts (living). Network scientist and a founder of computational social science at the University of Pennsylvania, who has argued that digital records could let social science become genuinely cumulative and solution-oriented. ORCID - Google Scholar - Faculty Page - Wikipedia

Max Weber (1864-1920). German sociologist and political economist who founded the interpretive (verstehen) tradition and argued for a value-free social science that explains action through the meanings actors attach to it. Wikipedia - Wikidata

Frequently Asked Questions

What are the social sciences? They are the disciplines that study human beings in their social aspect: how people associate, govern, trade, and make meaning together. They include sociology, anthropology, economics, political science, and demography, and MeSH classifies the group among the behavioral sciences alongside psychology.

How do the social sciences differ from the natural sciences? Their subject is reflexive: people hold beliefs about themselves, respond to being studied, and vary across cultures and eras in ways physical systems do not. This makes universal, context-free laws rare and gives interpretation, the understanding of why people act, a role it does not have in physics.

What is positivism? Positivism is Auguste Comte's doctrine that society can be studied with the observational, law-seeking methods of the natural sciences to yield objective knowledge of social regularities. It was the founding program of sociology and remains one pole of a lasting debate about social-science method.

What is the difference between Durkheim's and Weber's approaches? Durkheim treated social facts as external things to be explained by other social facts, a broadly positivist program. Weber argued that explaining human action also requires verstehen, grasping the subjective meaning an act holds for the person, which makes his approach interpretive as well as causal.

What is construct validity? It is the degree to which a test measures the abstract construct it claims to, such as intelligence or anxiety. Because such constructs have no single observable criterion, validity is established by showing the measure relates to other variables as the underlying theory predicts.

What is the replication crisis? It is the discovery, prominent from the 2010s, that a large share of published findings in psychology and related fields fail to reproduce when the studies are repeated. A major collaborative project found that only a minority of 100 replicated psychology studies yielded significant results the second time.

What does WEIRD mean in social science? WEIRD stands for Western, Educated, Industrialized, Rich, and Democratic, the kind of participant who dominates behavioral research. Because such people are atypical on many psychological measures, findings drawn from them may not generalize to the rest of humanity.

What is computational social science? It is the study of human behavior through the large-scale digital traces people leave in email, mobile, and online activity, using computational methods. It promises population-scale observation of social life but faces obstacles of data access, privacy, and reproducibility.

References