Repeated Measures Workflow

How to analyze repeated measurements from the same subjects in Licklider, from identifying the data structure to choosing between paired tests, Friedman, and mixed models.

Repeated measures data occurs when the same subjects are measured more than once — across time points, treatment conditions, or both. Because observations from the same subject are correlated, they cannot be treated as independent, and standard group comparison tests do not apply directly.

This page describes how to handle repeated measures data in Licklider.

Identifying repeated measures data

Before running the analysis, Licklider needs to know that the data has a repeated structure. The key indicator is a subject ID column — a column that identifies which subject each row belongs to — where the same ID appears in more than one row.

If your dataset has a subject ID column, make sure it is identified in the Data Contract → see Variable and ID Mapping.

When Licklider detects that the same ID appears across multiple conditions or time points, it will ask you to confirm whether the design is repeated measures or independent samples → see Pseudoreplication Detection.

Data format

Long format (recommended)

One row per observation. Columns should include:

  • Subject ID
  • Time point or condition
  • Outcome value

Wide format

One row per subject, with one column per time point. Wide format can be reshaped to long format in the Prep panel before analysis → see Table Shape: Wide vs Long.

Choosing the right analysis

Two time points or two conditions

A paired comparison is appropriate. Licklider selects a paired t-test (for normal differences) or Wilcoxon signed-rank test (for non-normal) automatically when subject IDs are consistent across two conditions.

Request it with: "Compare before and after" or "Paired comparison between conditions A and B."

Three or more time points — non-parametric

The Friedman test evaluates whether the outcome differs across time points without assuming normality. Use this when normality cannot be assumed and covariates are not needed.

Request it with: "Run a Friedman test across the three time points."

Three or more time points — with covariates or complex design

A generalized linear mixed model (GLMM) with a random intercept accounts for the correlation between repeated observations from the same subject. Use this when:

  • Covariates need to be included
  • The design is unbalanced (subjects have different numbers of observations)
  • The outcome is binary (use Binomial family)

Request it with: "Fit a mixed model with subject as a random effect" or "Use a GLMM to account for repeated measurements."

For more detail → see Repeated Measures and Mixed Models and GLMM: Gaussian and Binomial.

Line plots for longitudinal data

For data with multiple time points, a line plot with individual trajectories (spaghetti plot) shows within-subject change more clearly than group means.

Request it with: "Show a spaghetti plot" or "Plot individual trajectories over time."

Common problems

Same ID appears in multiple groups unexpectedly

If subject IDs appear across groups but the design is intended to be independent, Licklider will flag this as a potential pseudoreplication concern. Confirm whether the design is paired or independent → see Paired vs Unpaired Guard.

Wide format data

If each time point is a separate column, reshape to long format before requesting the analysis → see Table Shape: Wide vs Long.

What this page does not cover