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SUMMARY:Building Better Questionnaires with Probabilistic Modelling - Rica
 rdo Silva\, UCL
DTSTART:20130430T130000Z
DTEND:20130430T140000Z
UID:TALK44156@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:With the advent of new technologies such as search engines and
  data-rich social networks\, there has been a major increase on the availa
 bility of indirect sources of measurement for social behaviour. Still\, qu
 estionnaires remain an important probe into the attitude\, preferences and
  other traits of populations of interest. This happens particularly in sci
 entific contexts such as psychometrics\, on surveying specific populations
  such as NHS staff members\, or as a complement to noisy data\, such as fo
 llowing up fMRI or social networks studies with questionnaires. As such\, 
 it is important to provide means to increase the quality of the data and t
 he analysis. Although there exists a rich literature on measuring latent t
 raits by a better design of questionnaires\, a combination of theory-drive
 n approaches and data-driven methods provides new exciting possibilities o
 f improvement. Here we cover two aspects. First\, it is of interest to kee
 p questionnaires short such that response rates are high and artefacts of 
 question ordering become less problematic. Designing shorter questionnaire
 s is closely related to machine learning approaches for active learning an
 d sampling\, as we will discuss. Second\, in many studies questions are de
 signed as a way of targeting a priori latent traits\, and this background 
 knowledge can be exploited in a latent variable model within the family of
  “small rank plus sparse structure” models\, and where algorithms base
 d on composite likelihood approaches lead to scalable inference.
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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