What Cross-Sectional Loneliness Surveys Cannot Tell Us About Technology

Ask whether social media causes loneliness, and most of the evidence offered in response is a single-timepoint survey: a sample of people answers questions about their platform use and their feelings of loneliness on the same day, and a correlation is reported. Cigna’s 2020 workplace survey found that 73% of workers aged 18 to 22 report loneliness. The Harvard Making Caring Common survey found 61% of young adults aged 18 to 25 reported serious loneliness in early 2021. Gallup’s 2023 global survey put the loneliest age band, 19 to 29, at 27%, against 17% among adults 65 and older. Each of these is a real finding about a moment. None of them can say what caused it.

This is not a minor caveat. It is the central limitation of the technology-and-loneliness literature, and it is worth being precise about why.

What a snapshot can and cannot show

A cross-sectional survey measures two things in the same people at the same time and reports how they move together. If heavy social media users in the sample also report more loneliness, three explanations remain equally live: technology use is driving the loneliness, loneliness is driving people toward technology use as a substitute or a search for connection, or some third factor – a stressful transition, a diagnosed mental health condition, a change in living situation – is driving both. The survey instrument cannot adjudicate between these, no matter how large the sample or how carefully the loneliness scale is validated.

The BMC Public Health review of the state of loneliness and social isolation research names inconsistent measurement as a barrier to comparing findings across studies, but the more basic problem sits upstream of measurement: most of the studies being compared share the same design, and that design was never built to answer a causal question in the first place.

The 2021 American Enterprise Institute survey on friendship illustrates the trap well. It found that 15% of men reported having no close friends in 2021, against 3% in 1990 – a fivefold increase over three decades. That is a striking number, and it arrived alongside the rise of smartphones and social platforms, which invites an obvious story. But the survey compares different people at two different points in time, not the same people followed across that period. It cannot rule out cohort effects, changes in how “close friend” is understood, or shifts in work and family structure that have nothing to do with a screen. The correlation between the timing of a technology shift and the timing of a social change is suggestive. It is not evidence of mechanism.

What longitudinal design adds

A longitudinal study follows the same individuals over time, measuring technology use and loneliness at multiple points. This does two things a single snapshot cannot. First, it establishes temporal order: did the change in use precede the change in loneliness, or the reverse? Second, because each person serves as their own comparison across waves, it substantially reduces the influence of stable individual differences – personality, baseline sociability, chronic health conditions – that confound between-person comparisons.

The JACSIS study of social isolation and loneliness in Japan during 2020 and 2021 is useful here precisely because of its design, not primarily its conclusions. By tracking the same respondents across two pandemic years rather than drawing fresh samples each time, it can speak to change within individuals over that period, which a repeated cross-sectional survey – even one run annually with the same questions – structurally cannot. The distinction matters because pandemic-era loneliness research is dense with studies that show prevalence rising between 2020 and 2021 without being able to say whether it rose more for the same people or because the composition of who was struggling shifted.

Nothing in the current technology literature has done the equivalent for social media and adolescent loneliness at the scale the question deserves. Jonathan Haidt’s account in “The Anxious Generation” assembles time-series data showing adolescent depression, anxiety, and loneliness rising roughly in step with smartphone and social media adoption from the early 2010s, and argues for a causal reading grounded in a shift from play-based to phone-based childhood. The companion evidence review compiled with Jean Twenge acknowledges directly that this remains contested: critics argue the population-level trends, however striking, are still aggregate correlations across cohorts rather than within-person tracking of use and mood over time, and that other explanations for the same decade’s mental health trends have not been ruled out. Aggregating across a generation is not the same operation as following individuals within it, even when both point to a similar decade.

Why remote work research shows the shape of the fix

A 2024 cross-sectional study of healthcare workers offers a smaller-scale illustration of what careful cross-sectional work can still do, even without a longitudinal design: it separated workplace isolation from loneliness as distinct constructs and found that perceived social support moderates how remote working relates to well-being. That is a meaningful analytic move – it stops the two concepts from collapsing into each other – but it still cannot establish that remote work caused isolation rather than isolated workers gravitating toward or being assigned remote roles. The same limitation applies, one level up, to any survey asking whether heavy platform use precedes or follows loneliness.

What would actually settle it

A study capable of answering the causal question would need to follow the same cohort across several years, measuring both technology use and validated loneliness instruments – ideally the UCLA Loneliness Scale, used in the AARP 45-plus survey and much of the academic literature, rather than a bespoke question set – at repeated intervals. It would need enough waves to detect whether change in use at one time point predicts change in loneliness at the next, controlling for each person’s own baseline. Better still would be a design with some source of variation in use that is not itself driven by prior loneliness – a platform policy change, a natural experiment in access – so that reverse causation can be ruled out rather than merely acknowledged.

Short of that, the honest reading of the current evidence is this: technology use and loneliness are correlated in most of the surveys that ask about both, the timing of aggregate trends is consistent with a causal story, and no study in wide circulation has the design to confirm that story over the alternative in which loneliness drives use, or a third factor drives both. That is not a reason to dismiss the concern. It is a reason to stop citing snapshot surveys as if they had closed a question that, on the evidence available by September 2024, remains open.