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A cross-sectional study measures different individuals during one observation period. Differences between ages can reflect confounding or birth-cohort differences as well as changes within individuals; a longitudinal study is needed to measure the latter directly.

A longitudinal study observes the same individuals repeatedly. Within-person changes distinguish temporal development from persistent differences between people, although calendar-period changes, selective dropout and measurement error still require attention. A random-intercept linear mixed model can represent persistent individual differences and correlated repeated measurements.

Use a longitudinal study with repeated measurements of the same individuals. Recruit people across several starting ages and measure the same ear, with a standardized anatomical definition and instrument, at baseline and subsequent visits over several years. Repeat measurements at each visit to estimate measurement error; where practical, conceal previous measurements from the observer. Record sex, body size, birth cohort and observer, and document reasons for loss to follow-up.

A simple random-intercept linear mixed model for ear length $Y_{ij}$ at elapsed follow-up time $t_{ij}$ isThe individual intercept ${\alpha }_i$ absorbs persistent differences between people. Alternatively, differences $Y_{ij}-Y_{i0}$ remove that intercept directly. Estimate the within-person regression coefficient $\beta$ and its confidence interval, accounting for the correlation of repeated measurements. Compare it with the cross-sectional slope rather than treating the two slopes as automatically identical.

This longitudinal study directly checks whether an individual's ears tend to lengthen as time passes. Several entry-age cohorts help assess whether the change varies with starting age. Calendar-period changes and selective dropout can still affect interpretation, so repeated observation alone does not eliminate every possible source of confounding or selection bias.

A cross-sectional age association need not equal within-person growth. The following alternatives each suggest a concrete extension of the study.

These explanations are hypotheses to be checked, not established facts about the participants. Neither a small p-value nor an approximately linear scatter plot excludes them.