Importing data

StatPrism reads spreadsheet exports — the kind you get from survey tools such as Google Forms, or any .xlsx / .csv file.

Opening a file

Use File ▸ Open… and choose your file. StatPrism supports:

  • .xlsx Excel workbooks (if a workbook has several sheets, you’ll be asked which one to load),

  • .csv files,

  • .sp StatPrism project files (these reopen a whole saved session — see Projects & settings).

The loaded data becomes the raw data at the start of your chain.

What a Google Forms export looks like

A Forms response sheet usually has:

  • a Timestamp column first,

  • one column per question, with the full question text as the header,

  • multiple-choice answers as plain text,

  • “select all that apply” (checkbox) answers as a comma-separated list in one cell,

  • linear-scale answers as numbers.

StatPrism reads all of this as-is. You don’t need to rename or reshape anything before importing.

The ID column

On import, StatPrism adds a mandatory ID column (1, 2, 3, …) as the first column. It uniquely identifies each respondent/row and is used, for example, to list which respondents are outliers. You don’t have to do anything with it.

If your file already contained a column literally named ID, it is kept but renamed out of the way so the automatic identifier can take that name. If you would rather use one of your own columns as the identifier, use the Select ID processing step (see Select ID).

Column types

Every column has a type that controls how StatPrism treats it and which analyses will accept it:

Numeric

Quantities and scores (age, income, test score, a 1–5 scale treated as a number).

Nominal

Unordered categories (gender, region).

Ordinal

Ordered categories (education level; a Likert scale treated as ordered labels).

ID

The identifier column.

On import, StatPrism infers a type from the data: numbers become Numeric, text becomes Nominal. Dates/timestamps are read as text.

Important

A blank answer in a text column is read as the literal value nan rather than a true “missing”. A blank answer in a numeric column stays genuinely missing. If you plan to analyse a question that has skipped answers, check how its blanks were read in the data viewer, and use Impute Missing or Filter if you need to handle them.

Setting a column to Ordinal (and its order)

Many questionnaire items are ordered (e.g. High school < Bachelor < Master < PhD). To have StatPrism respect that order in tables and charts, set the column to Ordinal and define the category order with the Transform Column step (see Transform Column). Ordinal columns are scored numerically where an analysis needs numbers (e.g. correlations), and keep their labels and order in frequency tables and pie charts.

Colour tags

Columns can carry a soft colour tag that follows them through the app and into exported Excel headers, which helps when you have many variables. Colours from a source sheet’s coloured header cells are picked up automatically on import.

Viewing and checking the data

Open the data viewer to see the imported table, confirm column types, and spot how blanks were read. After adding cleaning steps, previewing a step’s data shows the effect of that step — for example, Filter and Outliers show removed rows in red.

Replacing the data

You can load a different file at any time. Note that this resets the chain, so re-import before building your processing steps and analyses.