What SCCalc Does

SCCalc is a native macOS statistical computing app for desktop analysis work. It combines a spreadsheet-style data editor, variable metadata, guided analysis dialogs, a results workspace, and SCL automation in one project-oriented app.

The built-in procedures run directly on your Mac. You do not need to install R, Python, or a separate statistics runtime for supported analyses.

Use SCCalc when you need to:

Import or create tabular research, teaching, business, or survey data.
Review variable names, labels, types, measurement levels, and missing values.
Run common statistical procedures from guided dialogs.
Inspect warnings, tables, charts, and model summaries before reporting.
Export results for review, publication, teaching, or archiving.
Repeat a workflow with SCL scripts after data or options change.

System Requirements

macOS 26.0 or later.
Apple Silicon Mac.
8 GB RAM minimum, with 16 GB recommended for larger datasets.
Sufficient free disk space for project files, exports, and temporary reports.
Network access only when you want online help, support pages, or published documentation. The bundled manual and built-in analysis workflows are available offline.

First Launch

Open SCCalc from Applications.
Create a new project or open an existing project.
Import a CSV file, open sample data, generate a practice dataset, or paste tabular values into the data editor.
Review the sheet in Data Editor and switch to Variable View to check metadata before running inferential procedures.
Run an analysis from the Analysis menu.
Inspect the output in Results, including warnings and notes.
Export the result only after confirming it contains the intended data and no confidential information.

Recommended First Workflow

Start with sample data before using private or high-value files. A simple first run verifies file access, result rendering, and export permissions.

Choose File -> Open Sample Data.
Pick a small sample dataset.
Choose View -> Variable View and confirm the variable names and measurement levels look reasonable.
Choose Analysis -> Descriptive Statistics -> Descriptives.
Select a few numeric variables and run the procedure.
In Results, read the table from top to bottom and check whether any warnings were added.
Choose Results -> Export Results -> Export as PDF.
Open the exported PDF and confirm the tables, labels, and file destination are correct.

After this succeeds, repeat the same path with your own data. If something fails with your data but not with sample data, the issue is usually related to file encoding, variable metadata, missing values, unusual labels, or the analysis options selected for that dataset.

Project Workflow Map

Most projects follow this sequence:

Data source -> Data Editor -> Variable View -> Data checks ->
Analysis dialog -> Results -> Export, save, or automate with SCL

Move forward only when the current step is clean enough for the next step. For example, do not run a model until numeric variables imported as numbers, group variables have the expected categories, and missing-data handling is understood.

Offline And Online Help

The Help menu separates offline and online resources:

Help -> User Manual opens the offline PDF bundled with the app.
Help -> User Manual Online opens the latest published online manual.
Help -> SCCalc Help opens the searchable in-app help center.
Help -> Tutorials opens guided lessons and sample workflows.
Help -> Keyboard Shortcuts opens shortcut reference material.
Help -> SCL Quick Reference opens command syntax help for automation.
Help -> Command Reference opens the online command and analysis reference when network access is available.
Help -> Support opens current support information.

When working without a network connection, prefer the bundled manual, in-app help center, tutorials, sample data, and SCL Quick Reference. When network access is available, use the online manual for the latest published corrections and support links.

Privacy Check

Before copying, exporting, or sharing anything, pause and check whether the current view contains confidential information. Data values, variable labels, file paths, comments, output titles, charts, and screenshots can all reveal more than intended.

If you need help reproducing an issue, start with sample data. If the issue only appears in your own dataset, create a small synthetic file with the same column types and missing-data pattern instead of sending real participant, customer, student, patient, financial, institutional, or unpublished research data.

Getting Started