Missing Value Analysis

What it does

Reports missing values by variable, complete and incomplete rows and common missingness patterns.

Use it before any analysis, and before deciding how to handle gaps.

Data you need

• A dataset

Optional: Deletion or imputation, only if you choose it

Supported data: Any. Level: Beginner.

Formula and method

Missing % = 100 × missing / n

Each cell is marked observed or missing, then counted by variable and by row. Rows with the same combination of gaps form a pattern, so a repeated cause such as an optional survey section stands out.

How to read the result

A few repeated patterns often point to a data-collection cause.

Worked example

Illustrative run on Simulated study data (teaching data). These figures are the actual output of this model on that example, and they say nothing about your own data.

• 14 cells are missing; 226 of 240 rows are complete. satisfaction has the most missing values (14).
• No data were changed.

Limitations

• Counts cannot show why data are missing
• Mean or median filling understates uncertainty

Numerical validation

Validated 17 of 17 automated checks passed, including numerical results, invalid input, missing values, small samples, constant variables, charts and the interpretation text.

Computed with python 3.13.2, numpy 2.2.5, scipy 1.14.1, pandas 2.3.3, statsmodels 0.14.4, scikit-learn 1.7.0, in your browser. A passing check confirms the listed comparison only. It does not make any result suitable for your data, and it is not a claim of equivalence with other statistical software.

Questions

Will this change my data?

Only if you choose an action. Changes apply to a working copy, and Reset to original restores your upload.