Research CSV Quality Checker
Examine comma-separated research records for specific structural problems. The tool parses CSV in your browser and never uploads, saves or transmits the dataset. It cannot verify facts against original survey forms or identify the same person across different records.
Understand what each flag means
An empty field may mean no response, not applicable, or accidental omission; the checker cannot decide. Exact duplicate records can be deliberate repeated measurements; never delete them automatically. Mixed number-and-text values can mean inconsistent units, meaningful codes or a legitimate categorical variable, not necessarily bad input. A mismatch in the number of comma-separated fields is a structural error worth resolving before numerical analysis. Quoted commas, newlines and doubled quotation marks are parsed as CSV conventions, rather than split into artificial cells.
Use How to Organize a Reproducible Research Spreadsheet for a complete case study, change-log strategy and a data dictionary. Follow the student privacy checklist before collecting or sharing real records. To select a statistical procedure after cleaning, see the Statistical Test Decision Checker.
Source-backed methodology and limitations
The UK Government Data Quality Framework separates completeness, uniqueness, validity and accuracy. The checker focuses on observable formatting and count-based signals; accurate real-world measurements cannot be established from an internally consistent CSV alone. For CSV structure, see the W3C tabular data primer. Delimiters other than commas, spreadsheet formulas, character encoding repair and statistical imputation are outside this tool's scope. Imported data are read only during this browser session and should be cleared when finished.
Can this checker help with a small business CSV?
Yes, for authorized, non-sensitive exports such as a synthetic inventory sample or public product-price list. Structural checks for missing cells, uneven rows and repeated exact records also apply to business spreadsheets. However, matching duplicate lines does not prove duplicate invoices, and the checker cannot verify that a transaction amount is correct. Do not enter confidential customer, payroll, banking or employee data unless your organization has specifically approved the environment and handling.
Read AI for Excel and Google Sheets in Small Business for original formula examples, safe data-handling steps and an auditable workflow. See AI for Work & Business for our business ROI and freelance pricing tools. These are related applications of existing quality checks, not additional paid or AI-powered validation services.