Data Control Settings

Managing the privacy of submitted data

Vipula avatar
Written by Vipula
Updated over a week ago

This article details Qualee's data anonymity controls and logic:

  1. Data control & visibility

  2. Anonymity difference

  3. Comment visibility

  4. Scoring anonymity

  5. Topic generation


Preserving the anonymity of employees' responses to engagement survey questions provides a level of confidence that allows them to answer with honesty. The following control options provide the ability to fine tune the data visibility whilst still protecting the employee anonymity.

1. Data control & visibility

The segment identification levels under the Data control menu control the minimum number of individual employees that must have responded to a survey over a specified time period to allow the aggregated data to be visible. This is used to ensure the anonymity of employees.


NOTE: the Segment identification size sets the upper limit for Anonymity difference and Data availability.



Minimum Segment Size

Defines the minimum number of employee responses required for a segment to become visible for comparison. Setting a lower number reduces the relative anonymity, however doing so may be required for smaller teams.

E.g. Segment identification set to 4.

Segment - Team

Segment displayed

Finance | 4 survey responses

Finance | 3 survey responses

In this example, with less than 4 responses from employees in the Finance team, the Finance team would not be displayed in the Team segment.


2. Anonymity difference

Anonymity difference sets the minimum number of survey responses between two different groups of employees.

This is important to avoid responses from small groups being inferred by the responses of larger visible groups.

Scenario 1.a
• A team of 10 employees is attempted to be segmented by their gender.
• The responses are from 9 females and 1 male.
Segment identification (minimum segment size) is set to 4.

Result
As there are more than 5 responses, the female segment would be created, and by exclusion, it may be possible to deduce the response from the single male.

Scenario 1.b

• Same team of 10 employees: 9 females and 1 male.
Segment identification is set to 4.

Anonymity difference is set to 3.

Result
Both gender segments will not be displayed to prevent inference.
Segments will only be displayed if:
1. there is at least 4 responses (Segment identification), and
2. the alternate segment size is at least 3 (Anonymity difference).

Scenario 2.a
• An organisation of 200 people is attempting to segment data by gender.
• There are survey responses from 103 females and 97 males.
Segment identification (minimum segment size) is set to 6.
Anonymity difference is set to 5.

Result
The gender segments will be visible, as:
1. There are more than 6 responses in a segment, and
2. The smaller of the segments is greater than 5.


3. Comment visibility

Employee comments can provide excellent insights into specific engagement drivers. Qualee's algorithm optimises the visibility of comments whilst still preserving anonymity.

Only comments from survey rounds in which the total number of employees answered are above the chosen levels are displayed on the dashboard and reports.

This process ensures that:

• no comments are displayed if the number of employees that responded is below
the set anonymity level.

• comments can be viewed before the full completion of a round.


4. Scoring anonymity

The overall anonymity of individual responses is enhanced by requiring a significant number of responses with the following logic being applied:

• If the total number of employees that are included in the aggregated results is less
than the anonymity level, the full set of scores is not displayed.

• For individual drivers and sub-drivers, if the total number of responses is less than the Data availability setting, the scores for the driver/sub-driver are not displayed.


5. Topic generation

Qualee uses an advanced A.I. process to analyse received comments, which are then presented in groupings or topics. Topics help to quickly understand commonality in responses and improve the likelihood of creating more effective initiatives.


The following control are used in determining which comments are referenced in the generation of Topics.

  1. Comment responses from survey rounds that are older than the selected Data referencing settings are excluded.

  2. Comment responses from former employees, that left more than the Data expiry setting are excluded.

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