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	<title>Comments on: Embracing the Un-Science of Qualitative Research Part Two - Ever-Evolving Prototypes are Ace</title>
	<atom:link href="http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/feed/" rel="self" type="application/rss+xml" />
	<link>http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/</link>
	<description>pretty design pending</description>
	<pubDate>Thu, 20 Nov 2008 07:50:26 +0000</pubDate>
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		<title>By: Kristen</title>
		<link>http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-27375</link>
		<dc:creator>Kristen</dc:creator>
		<pubDate>Fri, 03 Aug 2007 19:02:46 +0000</pubDate>
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		<description>I agree with this 3 part series and look forward to the last part.  But my question is, what about generalizations?  Having rich data from a few participants is great, but what works for one doesn't always work for another. Can you then make generalizations? And if so, how can you be sure you're adequately representing the participant population?   And, in a formal environment, how can you justify your generalizations?</description>
		<content:encoded><![CDATA[<p>I agree with this 3 part series and look forward to the last part.  But my question is, what about generalizations?  Having rich data from a few participants is great, but what works for one doesn&#8217;t always work for another. Can you then make generalizations? And if so, how can you be sure you&#8217;re adequately representing the participant population?   And, in a formal environment, how can you justify your generalizations?</p>
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		<title>By: Embracing the Un-Science of Qualitative Research &#171; ilab</title>
		<link>http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-26595</link>
		<dc:creator>Embracing the Un-Science of Qualitative Research &#171; ilab</dc:creator>
		<pubDate>Sun, 29 Jul 2007 21:25:17 +0000</pubDate>
		<guid isPermaLink="false">http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-26595</guid>
		<description>[...] Embracing the Un-Science of Qualitative Research Part Two - Ever-Evolving Prototypes are Ace One of the fundamentals of quantitative research is its systematic nature. It&#8217;s about measuring stuff. And, you don’t want that stuff to change as you&#8217;re measuring it for a number of reasons&#8212;not the least of which being that it makes it very difficult to plot on a graph [...]</description>
		<content:encoded><![CDATA[<p>[...] Embracing the Un-Science of Qualitative Research Part Two - Ever-Evolving Prototypes are Ace One of the fundamentals of quantitative research is its systematic nature. It&rsquo;s about measuring stuff. And, you don’t want that stuff to change as you&rsquo;re measuring it for a number of reasons&mdash;not the least of which being that it makes it very difficult to plot on a graph [...]</p>
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		<title>By: Qualitative Research &#187; UIE Brain Sparks</title>
		<link>http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-25939</link>
		<dc:creator>Qualitative Research &#187; UIE Brain Sparks</dc:creator>
		<pubDate>Wed, 25 Jul 2007 17:05:22 +0000</pubDate>
		<guid isPermaLink="false">http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-25939</guid>
		<description>[...] You can read part 2 here: Embracing the Un-Science of Qualitative Research Part Two - Ever-Evolving Prototypes are Ace   Part 3 is on the way. [...]</description>
		<content:encoded><![CDATA[<p>[...] You can read part 2 here: Embracing the Un-Science of Qualitative Research Part Two - Ever-Evolving Prototypes are Ace   Part 3 is on the way. [...]</p>
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		<title>By: Michael Clarke</title>
		<link>http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-24818</link>
		<dc:creator>Michael Clarke</dc:creator>
		<pubDate>Tue, 17 Jul 2007 15:45:24 +0000</pubDate>
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		<description>Ever-evolving prototype - I do like that.  And if you're trying to hit a tough deadline, it's the only way to go.</description>
		<content:encoded><![CDATA[<p>Ever-evolving prototype - I do like that.  And if you&#8217;re trying to hit a tough deadline, it&#8217;s the only way to go.</p>
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		<title>By: Lance</title>
		<link>http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-24653</link>
		<dc:creator>Lance</dc:creator>
		<pubDate>Mon, 16 Jul 2007 15:34:46 +0000</pubDate>
		<guid isPermaLink="false">http://www.disambiguity.com/embracing-the-un-science-of-qualitative-research-part-two-ever-evolving-prototypes-are-ace/#comment-24653</guid>
		<description>I like your general take on this but have a comment on "validation."

I think you don't need statistical validity with observation-based testing because the validation is really the expertise of the expert. In other words, you observe something that is, like you said, a "big" or "obvious" issue. That assessment validates the finding.

However, with A/B testing, there's a big  caveat: are you measuring preference or performance? In other words, are you banking on what people say they like or say they'd do? Or are you measuring what you observed them doing, and are comfortable that that's what they'd do independent of any test effect?

I've always found that hard to achieve, and have generally avoided using 121 testing to find preference for one design/solution over another.</description>
		<content:encoded><![CDATA[<p>I like your general take on this but have a comment on &#8220;validation.&#8221;</p>
<p>I think you don&#8217;t need statistical validity with observation-based testing because the validation is really the expertise of the expert. In other words, you observe something that is, like you said, a &#8220;big&#8221; or &#8220;obvious&#8221; issue. That assessment validates the finding.</p>
<p>However, with A/B testing, there&#8217;s a big  caveat: are you measuring preference or performance? In other words, are you banking on what people say they like or say they&#8217;d do? Or are you measuring what you observed them doing, and are comfortable that that&#8217;s what they&#8217;d do independent of any test effect?</p>
<p>I&#8217;ve always found that hard to achieve, and have generally avoided using 121 testing to find preference for one design/solution over another.</p>
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