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Data cleaning and preparation

Data Cleaning and Coding Tool

Prepare raw survey or spreadsheet data for analysis. Set variable types, labels and value labels, find and fix problems automatically, recode and reverse-code items, compute scale scores, and export clean data or send it straight to any tool on this site.

Free · No sign-upReplaces SPSS · OpenRefineApp · Works with your data
Updated · By Bibin Xavier

1. Load your data

Or drop a CSV, TSV or Excel file here.
Your data stays in this browser tab. Nothing is uploaded.
Variable viewRename variables, add labels, set type and measurement level, define value labels and missing codes, like SPSS.
Health checkFinds missing-value codes, stray spaces, inconsistent spelling, numbers stored as text, duplicates, outliers and straight-lining, with one-click fixes.
Recode and computeReverse-code Likert items, recode or merge categories, compute scale means, bin, dummy-code and standardize.
Export anywhereDownload clean CSV or Excel, a codebook and SPSS syntax, or send the data straight to any tool on this site.

How to cite this tool

Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.

APA 7

Xavier, B. (2026). Data Cleaning and Coding Tool [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/data-cleaning-tool/

BibTeX 

@software{xavier_datacleaning_2026,
  author = {Xavier, Bibin},
  title = {Data Cleaning and Coding Tool},
  publisher = {FreeStatisticalTools.com},
  year = {2026},
  url = {https://freestatisticaltools.com/data-cleaning-tool/},
  note = {Web application}
}

Suggested sentence for your methods section: “The analysis was carried out with the free Data Cleaning and Coding Tool tool on FreeStatisticalTools.com (Xavier, 2026).”

What is data cleaning and coding?

Data cleaning means finding and fixing the problems that make raw data unready for analysis: missing-value codes such as -99, inconsistent spelling, numbers stored as text, duplicate responses, out-of-range values and outliers. Data coding means giving every variable a clear name, type and measurement level, and turning answers into numbered categories with value labels. This tool does both in your browser, keeps a log of every step, and exports clean data with a codebook and SPSS syntax.

When to use data cleaning and coding

Use it before any analysis of survey, experiment or spreadsheet data: after downloading responses from Qualtrics, Google Forms, SurveyMonkey or Excel, and before running reliability, factor analysis, regression or SEM.

How to set up your data

One row per respondent or case and one column per variable, with variable names in the first row. Missing answers can be blank cells or codes such as -99; both are handled.

How to use this tool

  1. Load your data. Paste from Excel or Google Sheets, upload a CSV or Excel file, or open the messy example to see what the tool catches.
  2. Run the health check. It lists missing-value codes such as -99, stray spaces, inconsistent spelling, numbers stored as text, duplicates, outliers and straight-lining, each with a one-click fix.
  3. Define your variables. In the variable view, give variables clear names and labels, set numeric, text or date type and the measurement level, and add value labels such as 1 = Strongly disagree.
  4. Recode and compute. Reverse-code negatively worded items, merge categories, bin ages into groups, create dummy variables and compute scale means such as mean(SAT1 to SAT6).
  5. Export or analyse. Download a clean CSV or Excel file with a codebook and SPSS syntax, or send the data to a t-test, regression, factor analysis or PLS-SEM in one click.

How to report data cleaning and coding in APA style

Describe your data screening in the method section: how many cases were removed as duplicates, incomplete or straight-lining responses, how missing values were handled, and which items were reverse-coded. The cleaning log lists every step for you.

Frequently asked questions

How do I clean survey data for analysis?

Load the file, run the health check and apply its fixes, define missing-value codes, reverse-code negatively worded items, remove duplicates and straight-lining respondents, then compute scale scores. Export the clean CSV or send it straight to an analysis tool.

How do I reverse-code Likert items?

Open Clean & transform, choose Reverse-code items, select the negatively worded items and set the scale minimum and maximum (for example 1 and 5). New values are min + max − old, so 1 becomes 5 and 2 becomes 4, and value labels are reversed too.

What is a variable view?

A table with one row per variable, as in SPSS, where you set the name, label, type (numeric, text or date), measurement level (scale, ordinal or nominal), value labels and missing-value codes.

How do I recode values or merge categories?

Use Recode values with rules such as “1,2 = 1”, “3 thru 5 = 2” or “"Male","M" = 1”, into the same or a new variable, with optional labels for the new codes.

How do I calculate a scale score?

Use Compute variable with an expression such as mean(SAT1 to SAT6). mean() skips missing values, and you can require a minimum number of answered items.

Is my data uploaded anywhere?

No. Everything happens in your browser tab, and the data is gone when you close it. When you send data to another tool, it travels only within the same tab.

Can I export to SPSS?

Yes. Download the clean CSV together with the SPSS syntax file, which reads the CSV and applies your variable labels, value labels and measurement levels.

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Little’s MCAR Test and Missing Data AnalysisMissing values per variable, missing-data patterns, Little’s MCAR test and EM estimates of means and correlations.Outlier Calculator (Z-Score, IQR, Grubbs and Mahalanobis)Find univariate outliers with z-scores, Tukey’s IQR fences and Grubbs’ test, and multivariate outliers with Mahalanobis distance.

Browse all categories

Data cleaning and preparation · Describe and explore · Compare means · Nonparametric tests · Correlation and regression · Bayesian statistics · Categorical data · Reliability and agreement · Multivariate analysis · Structural equation modeling · Econometrics and time series · Panel data and causal inference · Marketing and decision research · Survival, clinical and meta-analysis · Study planning and calculators · PLS-SEM