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	<title>Five Sketches™ &#187; quantitative research methods</title>
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	<description>Ideation, design, and usability for development teams</description>
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		<title>Unreliability of self-reported user data</title>
		<link>http://fivesketches.com/2009/08/self-reported-user-data/</link>
		<comments>http://fivesketches.com/2009/08/self-reported-user-data/#comments</comments>
		<pubDate>Fri, 21 Aug 2009 11:55:49 +0000</pubDate>
		<dc:creator>JeromeR</dc:creator>
				<category><![CDATA[Design, process, business]]></category>
		<category><![CDATA[data decomposition]]></category>
		<category><![CDATA[data quality]]></category>
		<category><![CDATA[data reliability]]></category>
		<category><![CDATA[data validity]]></category>
		<category><![CDATA[fuzzy-set data]]></category>
		<category><![CDATA[ignorance]]></category>
		<category><![CDATA[interaction design]]></category>
		<category><![CDATA[quantitative research methods]]></category>
		<category><![CDATA[real-time data collection]]></category>
		<category><![CDATA[risk assessment]]></category>
		<category><![CDATA[uncertainty]]></category>
		<category><![CDATA[usability research]]></category>
		<category><![CDATA[usability studies]]></category>
		<category><![CDATA[usability testing]]></category>

		<guid isPermaLink="false">http://fivesketches.com/?p=2518</guid>
		<description><![CDATA[Many people are bad at estimating how often and how long they&#8217;re on the phone. Interestingly, you can predict who will overestimate and who will underestimate their phone usage, according to the 2009 study, &#8220;Factors influencing self-report of mobile phone use&#8221; by Dr Lada Timotijevic et al. For this study, a self-reported estimate is considered  accurate [...]]]></description>
			<content:encoded><![CDATA[<p>Many people are bad at estimating how often and how long they&#8217;re on the phone. Interestingly, you can predict who will overestimate and who will underestimate their phone usage, according to the 2009 study, &#8220;<a title="Opens in a new window" href="http://www3.interscience.wiley.com/journal/120775816/abstract?CRETRY=1&amp;SRETRY=0" target="_blank">Factors influencing self-report of mobile phone use&#8221; by Dr Lada Timotijevic <em>et al</em></a>. For this study, a self-reported estimate is considered <span style="background-color: #808000;"> <span style="color: #ffffff;"><strong>accurate</strong></span> </span> if it is within 10% of the actual number:</p>
<p style="text-align: center;"><img class="alignnone size-full wp-image-2561" title="Defining 'accuracy'" src="http://fivesketches.com/wp-content/uploads/2009/08/scale-of-accuracy.png" alt="Defining 'accuracy'" width="379" height="47" /></p>
<table style="text-align: left;" border="1" cellspacing="0" cellpadding="0" width="98%">
<tbody>
<tr>
<th width="31%" valign="top"></th>
<th style="text-align: center;" width="23%" valign="top">Underestimated</th>
<th style="text-align: center;" width="23%" valign="top">Accurate</th>
<th style="text-align: center;" width="23%" valign="top">Overestimated</th>
</tr>
<tr>
<th style="text-align: left;" valign="top">Number of phone calls</th>
<th style="text-align: center; font-size: 65%;" valign="middle">(number of people)</th>
<th style="text-align: center; font-size: 65%;" valign="middle">(number of people)</th>
<th style="text-align: center; font-size: 65%;" valign="middle">(number of people)</th>
</tr>
<tr>
<td style="text-align: left;" valign="top">High user</td>
<td style="text-align: center;" valign="top">71%</td>
<td style="text-align: center;" valign="top">10%</td>
<td style="text-align: center;" valign="top">19%</td>
</tr>
<tr>
<td style="text-align: left;" valign="top">Medium user</td>
<td style="text-align: center;" valign="top">53%</td>
<td style="text-align: center;" valign="top">21%</td>
<td style="text-align: center;" valign="top">26%</td>
</tr>
<tr>
<td style="text-align: left;" valign="top">Low user</td>
<td style="text-align: center;" valign="top">33%</td>
<td style="text-align: center;" valign="top">16%</td>
<td style="text-align: center;" valign="top">51%</td>
</tr>
<tr>
<th style="text-align: left;" valign="top">Duration of phone calls</th>
<th valign="top"></th>
<th valign="top"></th>
<th valign="top"></th>
</tr>
<tr>
<td style="text-align: left;" valign="top">High user</td>
<td style="text-align: center;" valign="top">41%</td>
<td style="text-align: center;" valign="top">20%</td>
<td style="text-align: center;" valign="top">39%</td>
</tr>
<tr>
<td style="text-align: left;" valign="top">Medium user</td>
<td style="text-align: center;" valign="top">27%</td>
<td style="text-align: center;" valign="top">17%</td>
<td style="text-align: center;" valign="top">56%</td>
</tr>
<tr>
<td valign="top">Low user</td>
<td style="text-align: center;" valign="top">13%</td>
<td style="text-align: center;" valign="top">6%</td>
<td style="text-align: center;" valign="top">81%</td>
</tr>
</tbody>
</table>
<p>If people are bad at estimating their phone use, does this mean that people are bad at all self-reporting tasks?</p>
<p>Not surprisingly, it depends how long it&#8217;s been since the event they&#8217;re trying to remember. It also depends on other factors. Here are some factoids that should convince you to be careful with self-reported user data that you collect.</p>
<h5>What&#8217;s the problem with self-reported data?</h5>
<p>On questions that ask respondents to remember and count specific events, people frequently have trouble because their ability to recall is limited. Instead of answering &#8220;I&#8217;m not sure,&#8221; people typically use partial information from memory to construct or infer a number. In 1987, N.M. Bradburn <em>et al</em> found that U.S. respondents to various surveys had trouble answering such questions as:</p>
<ul>
<li>During the last 2 weeks, on days when you drank liquor, about how many drinks did you have?</li>
<li>During the past 12 months, how many visits did you make to a dentist?</li>
<li>When did you last work at a full-time job?</li>
</ul>
<p>To complicate matters, not all self-report data is suspect. Can you predict which data is likely to be accurate or inaccurate?</p>
<ul>
<li>Self-reported Madagascar crayfish harvesting—quantities, effort, and harvesting locations—collected in interviews was shown reliable (2008, Julia P. G. Jones <em>et al</em>).</li>
<li>Self-reported eating behaviour by people with binge-eating disorders was shown &#8220;acceptably&#8221; reliable, especially for bulimic episodes (2001, Carlos M. Grilo <em>et al</em>).</li>
<li>Self-reported condom use was shown accurate over the medium term, but not in the short term or long term (1995, James Jaccard <em>et al</em>).</li>
<li>Self-reported numbers of sex partners were underreported and sexual experiences and condom use overreported a year later when compared to self-reported data at the time (2002, Maryanne Garry <em>et al</em>).</li>
<li>Self-reported questions about family background, such as father&#8217;s employment, result in &#8220;seriously biased&#8221; research findings in studies of social mobility in The Netherlands—by as much as 41% (2008, Jannes Vries and Paul M. Graaf).</li>
<li>Participation in a weekly worship service is overreported in U.S. polls. Polls say 40% but attendance data says 22% (2005, C. Kirk Hadaway and Penny Long Marler).</li>
</ul>
<h5>Can you improve self-reported data that you collect?</h5>
<p>Yes, you can. Consider these:</p>
<ul>
<li><strong>Decomposition into categories</strong>. Estimates of credit-card spending gets more accurate if respondents are asked for separate estimates of their expenditures on, say, entertainment, clothing, travel, and so on (2002, J. Srivastava and P. Raghubir ).</li>
<p>For your quantitative or qualitative usability research or other user research, it&#8217;s easy to write your survey questions or your lines of inquiry so they ask for data in a decomposited form.</p>
<li><strong>Real-time data collection</strong>. Collecting self-reported real-time data from patients in their natural environments &#8220;holds considerable promise&#8221; for reducing bias (2002, Michael R. Hufford and Saul Shiffman).<br />
<img class="alignnone size-full wp-image-2607" title="Collecting real-time self-report data" src="http://fivesketches.com/wp-content/uploads/2009/08/text-messaging.png" alt="Collecting real-time self-report data" width="400" height="73" /></li>
<p>This finding is from 2002. Social-media tools and handheld devices now make real-time data collection more affordable and less unnatural. For example, use text messages or <a title="Opens in a new window" href="http://en.wikipedia.org/wiki/Twitter" target="_blank">Twitter</a> to send reminders and receive immediate direct/private responses.</p>
<li><strong>Fuzzy set collection methods</strong>. Fuzzy-set representations provide a more complete and detailed description of what participants recall about past drug use (2003, Georg E. Matt <em>et al</em>).</li>
<p>If you&#8217;re afraid of math but want to get into fuzzy sets, try a textbook (for example, <a title="Opens in a new window" href="http://www.press.uchicago.edu/presssite/metadata.epl?mode=synopsis&amp;bookkey=39332" target="_blank">Fuzzy set social science</a> by Charles Ragin), audit a fuzzy-math course for social sciences (auditing is a low-stakes way to get things explained), or hire a tutor in math or sociology/anthropology to teach it to you.</ul>
<p>Also, when there&#8217;s a lot at stake, use multiple data sources to examine the extent of self-report response bias, and to determine whether it varies as a function of respondent characteristics or assessment timing (2003, Frances K. Del Boca and Jack Darkes). Remember that your qualitative research is also one of those data sources.</p>
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