🦉 🎤   RapidMiner Wisdom 2020 - CALL FOR SPEAKERS   🦉 🎤

We are inviting all community members to submit proposals to speak at Wisdom 2020 in Boston.

Whether it's a cool RapidMiner trick or a use case implementation, we want to see what you have.
Form link is below and deadline for submissions is November 15. See you in Boston!


evaluation of survey

currantcurrant Member Posts: 14 Contributor II
edited November 2018 in Help
Dear All,

in our company we conducted a survey among the employees (250 employees) . The participants could answer some questions to different topics (e.g. topic A: "communication", topic B: "Own actvities", topic C: "direct superior" (= direct boss), ... )
The possible answers (nominal) were "not relevant at all" (= 1 point), ..., "applies completely" (= 5 points).

Now, I want to find groups of employees within our company (e.g. -> satisfied employees, ...)

Up-to-now, I tried k-means: I calculated the average points for each employee within each topic and ran the k-means. here I found four groups (the best number of groups).

Are there any other approaches (PCA, ...) to find groups within the results of the survey?

Thanx in advance



  • MariusHelfMariusHelf RapidMiner Certified Expert, Member Posts: 1,869   Unicorn
    Hi currant,

    if you could calcuate the average for each employee, it means that you already transformed the nominal user input to numerical values (1-5 for each answer). On these values you could try a clustering algorithm like k-means *without* prior averaging the values. That way you use more information you gathered by your survey than with the averaging approach.

Sign In or Register to comment.