The Dummy Coding Tab

The Dummy Coding tab converts categorical variables (nominal/ordinal scale) into numeric dummy variables (0/1). Use this to prepare categorical variables as predictors for regression analysis or GLM.

Basic Usage

Opening Dummy Coding

Select Data > Dummy Coding... from the menu bar to open a new Dummy Coding tab.

Configuring the Transformation

1. Select the target dataset from the Dataset dropdown
2. Configure each column's Scale and Action, and optionally the Reference (reference category), in the column table
3. Review each column's reference category and the number of dummy variables in the Encoding Preview section
4. Click Create Dataset
5. Enter a dataset name and click OK

In the Encoding Preview, check that the unique value count is what you expect, with no extras from typos or inconsistent labels, and that the reference category is the one you intend. A column with only one unique value cannot be encoded and shows an error.

Sample Data Used in This Page

The examples on this page use a survey dataset (survey.csv) for 5 people. blood_type and education are categorical variables.

Column Configuration

Configure the Scale, Action, and Reference for each column.

Scale

Even on a column set to the ordinal scale, the defined order (low → mid → high, for example) is not used in dummy coding. Both the order in which dummy variables are generated and the default reference category follow the alphabetical order of the category names. To base the coding on a specific category, select it in Reference.

The initial Scale is the measurement scale set on the column. For columns without a scale, it is inferred from the data type: string and boolean columns become nominal, and numeric and date/time columns become interval. See Data Types and Measurement Scales for details. Change the Scale from the dropdown as needed. A code recorded as a number — an equipment or line number, for example — is inferred as interval, but changing its Scale to nominal makes it available for dummy coding.

Action

Dummy code options are only available for categorical columns (nominal/ordinal). Boolean columns are excluded¹.

Reference

The reference category is the group that dummy variable coefficients are compared against. Choosing a meaningful baseline — a control group, a standard condition, or a pre-intervention level — makes the coefficients easier to interpret.

For columns with the Action set to Dummy code or Dummy code (keep original), select the reference category from the Reference dropdown. The default is the alphabetically first category.

How It Works

From k categories, k-1 dummy variables are generated, omitting one category as the reference category². This scheme is called treatment coding (coding relative to a reference category).

1. Extract unique category values³
2. Sort alphabetically
3. Exclude the reference category. The default is the alphabetically first category, which can be changed from the Reference dropdown
4. Generate a dummy variable for each of the remaining k-1 categories
5. Code 1 for matching rows, 0 otherwise

Example

Converting the blood_type column (unique values: A, AB, B, O):

• Reference category: A (the default, alphabetically first)
• Dummy variables generated: blood_type_AB, blood_type_B, blood_type_O

Rows with the reference category A have all dummy variables set to 0.

When the dummy variables are used in a regression model, each dummy variable coefficient is an estimate of the difference from the reference category. The scale on which that difference is measured depends on the model's link function. In linear regression (identity link), it is a difference on the scale of the response variable itself. In GLMs with a non-identity link — logistic regression, Poisson regression — it is a difference on the scale of the linear predictor, such as log-odds or a log rate. In this example, fitting blood_type_B in a linear regression gives a coefficient that estimates the difference in the response variable between type B and the reference type A. In a model with other predictors, this is the difference holding them constant.

Output Dataset

With the sample data above, setting name to Not included, age to Include as-is, and both blood_type and education to Dummy code produces the following output.

blood_type (4 categories) produces 3 dummy variables, and education (3 categories: college, graduate, high_school; reference: college) produces 2. Column names follow the {original_column_name}_{category_name} format, with data type int64 (0 or 1) and a ratio measurement scale. They go straight into regression or GLM as predictors and are not dummy coded again. Row count stays the same. The original dataset is not modified; results are saved as a new derived dataset.

Editing Saved Dummy Coding

Existing Dummy Coding datasets can be edited. Right-click a dataset in the Project Lineage tab, or open the dataset menu (⋮) in Project Overview, and select Edit Operation... to open Dummy Coding in edit mode with the source dataset, column actions, reference categories, and scale overrides restored.

In edit mode, if other datasets, models, or reports depend on this dataset, a warning is displayed showing the type and count of affected items. Changing the source dataset carries over the settings of columns that exist in the new source under the same name. Actions of columns that are no longer categorical change to Include as-is, and reference categories that cannot be confirmed in the new source are reset. Settings that could not be carried over are listed in a notice. Click Update Dataset to apply the changes. Derived datasets that depend on this dataset are recalculated the next time their data is needed, and dependent models are automatically re-estimated.

If the transformation is changed from another path such as another edit tab or the Agent API between the time this tab opens and the time you click Update Dataset, MIDAS refuses the save. This keeps the earlier change from being lost without warning. Close the edit tab and choose Edit Operation... again to start over from the current version.

Next steps

• Linear Regression - Regression analysis using dummy variables
• The GLM Tab - GLM using dummy variables

See also

• The Convert Column Types Tab - Converting data types

Footnotes

1. Boolean columns are already equivalent to 0/1, so select Include as-is to use them directly. ↩

2. Creating dummy variables for all k categories would make their sum equal 1 in every row. The intercept is also 1 in every row, so the two would be linearly dependent and the regression coefficients could not be estimated uniquely. Omitting one category avoids this problem. ↩

3. Missing values are not counted as unique values. Rows where the original column is missing have missing values in all generated dummy variables and are dropped by listwise deletion in later regression or GLM analyses. Columns with only one unique value cannot be dummy coded. ↩

Also available as a Markdown file.