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Numeric Factors

Numeric Factors let you combine related columns into a single score.
They are useful anywhere you want one composite metric from multiple inputs, including:

  • Rating-style questions (e.g. satisfaction or agreement items)
  • Frequency/behavior measures (e.g. usage across several actions)
  • Readiness or maturity indices (e.g. process, capability, adoption)
  • Experience or risk scores built from multiple indicators

You create a new calculated column, choose the source columns, and control how the factor is calculated.

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Numeric factors walkthrough

Where to find it

  1. From the dashboard, click More.
  2. Click Calculated Column.
  3. Choose Numeric Factor.
  4. Click Continue.

Before you start

Numeric Factors work best when:

  • Your selected columns measure the same concept.
  • Higher values should generally mean "more" of that concept.
  • You select at least 2 source columns.

Supported source column types include opinion scale columns, numeric columns, and categorical columns that have assigned numbers.

How to create a Numeric Factor

  1. Enter a column name (for example, Combined Ratings).
  2. Under Source Columns, select all questions you want to include.
  3. In Reverse scoring, enable reverse scoring for items where higher values are actually negative.
  4. Choose Aggregate method:
    • Mean: sensitive to small differences.
    • Median: more robust to extreme responses.
  5. Set Minimum answered columns per row:
    • Require all selected: strict, most complete.
    • Require at least N columns: allows partial completion.
  6. (Optional) enable Normalize result to 0-100.
  7. Review Live reliability (Cronbach's alpha).
  8. Click Continue, check the preview, then click Create Column.

How to edit a Numeric Factor

After you create a Numeric Factor, it appears as a normal numeric column in your project. You can change its setup (sources, reverse scoring, aggregation, missing-data rules, normalization, name) without rebuilding from scratch.

  1. Put the factor column on a chart as the pivot (or open it from the legend where it applies).
  2. Choose Edit Column (for example from More on the dashboard, or the chart actions menu—depending on your layout).
  3. The Calculated Column editor opens on the configuration step.
  4. Adjust any settings, then click Update Column.

You can also delete the factor from that same editor when editing (Delete Column).

Note: In Manage Columns, calculated columns are shown as read-only summaries. To change how the factor is computed or how it is binned for charts, use Edit Column so the full editor opens.

Pivot and chart binning

A Numeric Factor is stored as a numeric column. In charts and pivots, numeric columns are grouped into bins (ranges) so bars and tables stay readable.

  • When you first create a factor, binning uses the same defaults as other numeric columns (typically automatic bins) until you change them.
  • After the factor exists, open Edit Column again and scroll to Pivot & chart binning. There you can choose modes such as auto bins, fixed width, equal frequency, or custom edges—same controls as for other numeric columns—with a histogram preview based on the factor’s current values.

For a full explanation of binning modes and tips, see Number binning.

Understanding the key options

Reverse scoring

Use this when a question is phrased in the opposite direction.

Example:

  • "I find this process easy" (higher is positive)
  • "I find this process frustrating" (higher is negative)

If both are included in one factor, reverse-score the negative item so all columns point in the same direction.

Aggregate method (Mean vs Median)

  • Mean is best when you want every response shift to influence the score.
  • Median is best when you want a stable center score that is less affected by extreme values.

Minimum answered columns

This controls missing-data behavior:

  • Require all selected gives stricter, cleaner factor scores but may exclude more rows.
  • Require at least N keeps more rows by allowing partially answered item sets.

Normalize result to 0-100

Enable this when source columns have different scales (for example, one item is 1-5 and another is 0-10). AddMaple rescales each selected item before combining, so the final factor stays on a consistent 0-100 scale.

Reliability check (Cronbach's alpha)

Numeric Factor setup shows a live Cronbach's alpha estimate (with a short consistency label and an expandable explanation of common interpretation bands):

  • Higher alpha generally means the selected items move together consistently.
  • Very low alpha can indicate your selected columns are not measuring the same underlying concept.

Use this as a quick quality check before finalizing the column. In plain terms, if this score is low, your selected questions may not belong in one combined score.

Example workflows

You have 6 experience ratings:

  • Ease of use
  • Reliability
  • Speed
  • Value for money
  • Support quality
  • Overall satisfaction

You can combine all six into one factor called Combined Ratings, normalize to 0-100, and then use that new numeric column in pivots, charts, filters, and comparisons like any other numeric field.

Another common use is a product-adoption score:

  • Weekly usage frequency
  • Feature breadth used
  • Self-reported confidence
  • Task completion speed

Even when these come from different scales, you can normalize to 0-100 so each input contributes comparably.

Key points

  • Numeric Factors create one reusable numeric score from multiple related columns.
  • Use Edit Column to update the factor or configure Pivot & chart binning once the column exists.
  • Reverse scoring is important for negatively worded items.
  • Missing-data rules (Require all vs Require at least) can materially change coverage.
  • Cronbach's alpha helps validate whether your selected items belong together.