In wide format data, how are repeated measures across time points typically stored?

In wide format data, how are repeated measures across time points typically stored?

• A. Data are arranged so that scores from a single entity appear in a single row and levels are in different columns.
• B. Data are arranged so that each measurement is in a separate row.
• C. Repeated measures for a single entity appear in separate columns.
• D. Data are arranged with each predictor variable in its own row.

Answer: (C)

In wide format, you keep one row per entity and put each time-point measurement in its own column. So a subject would have scores for time points across separate columns (for example, score_T1, score_T2, score_T3), all in the same row with the subject’s other attributes. This setup contrasts with long (tall) format, where each measurement would occupy its own row and time would be coded as a separate variable. The wide arrangement is exactly what allows analyses that treat time points as multiple measurements of the same subject within a single row, such as repeated-measures ANOVA in SPSS, where each time point corresponds to its own column.

In wide format, you keep one row per entity and put each time-point measurement in its own column. So a subject would have scores for time points across separate columns (for example, score_T1, score_T2, score_T3), all in the same row with the subject’s other attributes. This setup contrasts with long (tall) format, where each measurement would occupy its own row and time would be coded as a separate variable. The wide arrangement is exactly what allows analyses that treat time points as multiple measurements of the same subject within a single row, such as repeated-measures ANOVA in SPSS, where each time point corresponds to its own column.