Updating the paragraph on response rates

design/design.html

@@ -801,13 +801,19 @@ <h1>9 Make sure response rates do not differ as a function of treatment
 biased away from the true treatment effect—we lack data on those with
 the highest potential outcomes, those subjects who have been exposed to
 the strongest version of the treatment.</p>
-<p>One way to deal with this in the design of your survey (not the
-design of the treatment or in analysis alone [e.g., using bounded
-treatment effects<a href="#fn7" class="footnote-ref" id="fnref7"><sup>7</sup></a>]), is to track a subsample of individuals
-from the hard-to-reach group. Choose a subset of missing respondents and
-invest in tracking and reaching them. At the analysis stage you have the
-option of weighting the data from this subsample in order to account for
-attrition.</p>
+<p>Our guide <a href="https://egap.org/resource/10-things-to-know-about-missing-data/">10
+Things to Know About Missing Data</a> provides details on how to deal
+with problems of attrition in your analysis, e.g., by placing bounds on
+treatment effects.<a href="#fn7" class="footnote-ref" id="fnref7"><sup>7</sup></a> You can also deal with attrition through
+the design of your survey, by tracking a random subsample of individuals
+from the hard-to-reach group. Choose a random subset of missing
+respondents and invest in tracking and reaching them. At the analysis
+stage you can combine data from this subsample with the data from your
+main sample through a weighted average in order to obtain an estimate of
+the average outcome in the sample as a whole. This approach is often
+referred to as “double sampling.” See Lohr (2009, chap. 8.3)<a href="#fn8" class="footnote-ref" id="fnref8"><sup>8</sup></a> and our
+guide <a href="https://egap.org/resource/10-things-to-know-about-sampling/">10
+Things to Know About Sampling</a> for details.</p>
 </div>
 <div id="pilot" class="section level1">
 <h1>10 Pilot!</h1>
@@ -848,9 +854,11 @@ <h1>10 Pilot!</h1>
 Roth. “Measuring and bounding experimenter demand.” <em>American
 Economic Review</em> 108 (11) (2018): 3266-3302.<a href="#fnref6" class="footnote-back">↩︎</a></p></li>
 <li id="fn7"><p>This approach involves estimating the upper and lower
-“bounds,” which are the largest and smallest ATEs we would obtain if the
-missing information were filled in with the highest and lowest outcomes
-that appear in the data we have.<a href="#fnref7" class="footnote-back">↩︎</a></p></li>
+“bounds,” which are the largest and smallest treatment effect estimates
+that we would obtain if the missing information was equal to the highest
+and lowest possible values of our outcomes.<a href="#fnref7" class="footnote-back">↩︎</a></p></li>
+<li id="fn8"><p>Lohr, Sharon L. 2009. <em>Sampling: Design and
+Analysis.</em> 2nd ed. Boston: Brooks/Cole Cengage Learning.<a href="#fnref8" class="footnote-back">↩︎</a></p></li>
 </ol>
 </div>
 

design/design.rmd

@@ -150,9 +150,10 @@ Start your questionnaire with an introduction that makes the respondent feel inf
 ==
 If response rates are related to treatment status, your survey data can yield biased estimates. For example, let’s say you are conducting a study of an intervention that involves testing study participants for health problems and informing those in the treatment group of their diagnosis. This is a sensitive intervention and may cause people in the treatment group to become more apprehensive about answering a survey than those in the control group. Simply analyzing the data without accounting for differential response rates would yield data that was biased away from the true treatment effect—we lack data on those with the highest potential outcomes, those subjects who have been exposed to the strongest version of the treatment.
 
-One way to deal with this in the design of your survey (not the design of the treatment or in analysis alone [e.g., using bounded treatment effects[^7]]), is to track a subsample of individuals from the hard-to-reach group. Choose a subset of missing respondents and invest in tracking and reaching them. At the analysis stage you have the option of weighting the data from this subsample in order to account for attrition.
+Our guide [10 Things to Know About Missing Data](https://egap.org/resource/10-things-to-know-about-missing-data/) provides details on how to deal with problems of attrition in your analysis, e.g., by placing bounds on treatment effects.[^7] You can also deal with attrition through the design of your survey, by tracking a random subsample of individuals from the hard-to-reach group. Choose a random subset of missing respondents and invest in tracking and reaching them. At the analysis stage you can combine data from this subsample with the data from your main sample through a weighted average in order to obtain an estimate of the average outcome in the sample as a whole. This approach is often referred to as "double sampling." See Lohr (2009, chap. 8.3)[^8] and our guide [10 Things to Know About Sampling](https://egap.org/resource/10-things-to-know-about-sampling/) for details. 
 
-[^7]: This approach involves estimating the upper and lower "bounds," which are the largest and smallest ATEs we would obtain if the missing information were filled in with the highest and lowest outcomes that appear in the data we have.
+[^7]: This approach involves estimating the upper and lower "bounds," which are the largest and smallest treatment effect estimates that we would obtain if the missing information was equal to the highest and lowest possible values of our outcomes.
+[^8]: Lohr, Sharon L. 2009. *Sampling: Design and Analysis.* 2nd ed. Boston: Brooks/Cole Cengage Learning.
 
 10 Pilot!
 ==