In long format data, how are repeated measures typically stored?

In long format data, how are repeated measures typically stored?

• A. Repeated measures for a subject are stored in separate columns.
• B. Repeated measures are stored in a single column with a time indicator, and each observation is a row.
• C. Repeated measures for a subject are stored in multiple rows.
• D. Repeated measures are not allowed.

Answer: (C)

In long format, each measurement is its own row, so a subject measured at several time points appears as multiple rows. Each row includes the subject’s identifier, a time (or condition) indicator, and the measured value. This makes the time dimension explicit and keeps the data tidy for analyses that track within-subject changes, such as repeated-measures models. Storing repeated measures in separate columns would create a wide format, where different time points occupy different columns, not long format. The idea that repeated measures aren’t allowed is not true; long format routinely contains multiple rows per subject.

In long format, each measurement is its own row, so a subject measured at several time points appears as multiple rows. Each row includes the subject’s identifier, a time (or condition) indicator, and the measured value. This makes the time dimension explicit and keeps the data tidy for analyses that track within-subject changes, such as repeated-measures models. Storing repeated measures in separate columns would create a wide format, where different time points occupy different columns, not long format. The idea that repeated measures aren’t allowed is not true; long format routinely contains multiple rows per subject.