DonnaM » Blog Archive » A new card sorting tool

A new card sorting tool

My friends at Optimal Usability have released their brand-spanking new card sorting tool into beta release (it’s free until August).

Their tool, called OptimalSort, caters for open and closed card sorts. The sorting interface is a spatial drag-and-drop.

The thing I like best (and I will fully disclose that I was involved in this, so I should like it) is the analysis options. No stinkin dendrograms here, but lists of category names, detailed participant results and lots of ways to explore the data.

Go try OptimalSort.

5 Responses to “A new card sorting tool”

  1. Stephan Schillerwein ¦ Schillerwein Net Consulting Says:

    This looks real good and easy to use! :-)

    So far, I’ve been advising clients to use WebSort (websort.net) if they wanted to do online/remote card sorting. Can you tell me how the two compare/differ?

    Thanks a lot!
    Stephan

  2. Donna Maurer Says:

    OptimalSort has far better analysis capabilities. And I like the sorting method better – websort has some interaction oddities.

    But go and have a look. It is free at the moment.

  3. louise Says:

    Oooh, my god! The best stuff!

    [url=][/url]

  4. Veronica Hinkle Says:

    Hi, Donna. Great comments!

    Do you have any recommendations on making dendograms useful? I agree with some of the problems for “interpreting” dendograms, specially when the groups are not easy to distinguish because of poor (or lack of) color representation, etc.

    I personally like dendograms, but I do find that most designer’s mental models of how dendograms ought to look make them hard to use for analyzing card sorting results (even for those of us with statistical backgrounds!).

    I do wonder what are the top ten complaints that most people have with the use of dendograms for analyzing card sorting results. Do you have any ideas?

  5. Donna Maurer Says:

    Hi Veronica, sorry I’m slow to answer.

    The best way to make dendrograms useful is to:
    1. Understand the statistics behind them
    2. Vary the way ‘closeness’ is measured and generate a few different versions of the dendrograms
    3. Don’t look at one dendrogram, but at the differences generated by different algorithms

    And don’t collect more data than you need – that way it is easier to analyse anyway ;)

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