## 2 Related Literature  
There are many places to go for tips on survey design. [PERSON] (2005) [PERSON] (2005), for example, discusses considerations such as look and feel, ordering of questions, and branching options. The Harvard University Program on survey research offers a tip sheet to survey designers ([[http://psr.iq.harvard.edu/book/questionnaire-design-tip-sheet](http://psr.iq.harvard.edu/book/questionnaire-design-tip-sheet)]([http://psr.iq.harvard.edu/book/questionnaire-design-tip-sheet](http://psr.iq.harvard.edu/book/questionnaire-design-tip-sheet))). [PERSON] (2005) [PERSON] (2005) discussed a technique for reducing the number of components needed for calculating composite measures of personality, opinions and attitudes. In that work, surveys are shortened by eliminating questions. [PERSON] _et al._ (2015) [PERSON] _et al._ (2015) use itemized response theory models to similarly shorten a survey by eliminating questions. There are also many software tools that facilitate designing surveys when branching rules are known a-priori. For example, REDCap, Qaultrics, SurveyMonkey, FourEyes, and SurveyGizmo each provide the capability to build-in branching logic rules ([[http://project-redcap.org/](http://project-redcap.org/)]([http://project-redcap.org/](http://project-redcap.org/)), [[http://www.qualtrics.com](http://www.qualtrics.com)]([http://www.qualtrics.com](http://www.qualtrics.com)), [[http://www.surveymonkey.com](http://www.surveymonkey.com)]([http://www.surveymonkey.com](http://www.surveymonkey.com)), [[https://getfoureyes.com/p/charming/](https://getfoureyes.com/p/charming/)]([https://getfoureyes.com/p/charming/](https://getfoureyes.com/p/charming/)), [[http://www.surveygizmo.com](http://www.surveygizmo.com)]([http://www.surveygizmo.com](http://www.surveygizmo.com))). None of these software tools, however, addresses the same topic we address in this paper, which is how to _learn_ branching rules from historical surveys. The work in this paper has a relation to computer adaptive testing (CAT). The literature on CAT is scattered and vast. Good introductions to CAT are [PERSON] _et al._ (1994) [PERSON] _et al._ (2000) and [PERSON] (2000) [PERSON] (2000). Like CAT, we aim to shorten a survey for respondents by deciding which questions to present a respondent based on their evolving pattern of answers. Our approach uses a data mining technique to discover branching rules from past surveys. CAT, on the other hand, uses past surveys to build sophisticated statistical models that allow the question sequence to be customized for each respondent in order to match to their underlying ability level. Based on the response to each question, the respondent's estimated ability level is updated and used to select the next question to be presented. Questions are selected to reduce the uncertainty in the updated estimate of ability as much as possible. Surveys with CAT typically terminate when a specified amount of precision in the estimated ability is achieved. Compared to CAT, our data mining approach to shortening a survey is simpler for practitioners to use and is less encumbered by necessary adherence to modeling assumptions. A key feature of CAT is that each respondent's ability level is estimated with nearly the same precision. However, for some surveys this is not a crucial objective. Instead, the objective of many surveys is simply to gather diagnostic information from a respondent such as feedback about a program or indicators of a disease. In these situations, if time was not an issue each respondent would be asked all of the questions on the survey. Our data mining approach to developing branching rules is useful in these contexts. Our data mining approach to discovering branching rules has similarities to market basked analysis (MBA). A good introductory reference to MBA is [PERSON] and [PERSON] (2011) [8]. In the context of surveys, patterns of answers to the survey questions could be viewed as item sets.