How to Prepare an Excel Master Chart for Research Analysis

One row should represent one observation at the intended analysis level

For a participant-level cross-sectional study, a typical master chart has one row per participant and one column per variable. Use simple, distinct column names such as study_id, age_years, group, haemoglobin_g_dl and outcome_status. For repeated measurements, a long-format table may be required, with multiple rows per participant and a time-point column.

Illustrative fictional data, not patient records.

Build a data dictionary

For each column record a human-readable definition, type, units, permitted categories and how missing values are represented. Use consistent categories (Male and Female, rather than variations with spaces and different spellings) and avoid mixing numbers and labels in the same variable without documenting coding.

Before importing

• Remove names, phone numbers, addresses and other direct identifiers unless essential to an approved workflow; use a study code.
• Check duplicate IDs and unexpected duplicate rows.
• Confirm numeric units and plausible value ranges.
• Distinguish actual zero from missing or “not applicable.”
• Review whether the analysis dataset represents all enrolled participants or only those with complete data.
• Preserve an untouched copy of the original approved dataset.

Common structural mistakes

Merged cells, subtotals within raw data, one column holding multiple answers, multiple spreadsheets with inconsistent coding, and changing category labels mid-study. These are frequent reasons automated analysis generates misleading summaries.

Upload a prepared Excel or CSV file into ResearchTry Statistical Analysis and verify detected variable types before calculating results.

Frequently asked questions

Should a participant with multiple visits have one row?

It depends on the analysis grain. Long-format longitudinal data often use one row per participant-visit, with stable IDs and visit times.

May I upload identifiable patient spreadsheets?

Only when the platform and your institution explicitly permit it and appropriate privacy safeguards are in place; prefer de-identified data.

Further reading and primary guidance

• EQUATOR Network: STROBE observational study reporting guideline
• ICMJE manuscript preparation: statistics, tables and references