BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//conferences.acspri.org.au//2024//talk//UHGAYZ
BEGIN:VTIMEZONE
TZID:AEST
BEGIN:STANDARD
DTSTART:20000326T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3;UNTIL=20050326T170000Z
TZNAME:AEST
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
END:STANDARD
BEGIN:STANDARD
DTSTART:20060402T040000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060401T170000Z
TZNAME:AEST
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
END:STANDARD
BEGIN:STANDARD
DTSTART:20070325T040000
RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=3;UNTIL=20070324T170000Z
TZNAME:AEST
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
END:STANDARD
BEGIN:STANDARD
DTSTART:20080406T040000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4
TZNAME:AEST
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000827T030000
RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=8;UNTIL=20000826T170000Z
TZNAME:AEDT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20011028T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20071027T170000Z
TZNAME:AEDT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20081005T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10
TZNAME:AEDT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-2024-UHGAYZ@conferences.acspri.org.au
DTSTART;TZID=AEST:20241129T100000
DTEND;TZID=AEST:20241129T101500
DESCRIPTION:Nonresponse is a critical issue for data quality in panel surve
 ys. Many researchers have demonstrated the potential of machine learning m
 odels to predict nonresponse\, which would then allow survey managers to p
 re-emptively intervene with low-propensity participants. Typically\, model
 ers fit their machine learning models to the panel data that has accumulat
 ed several waves and report which algorithm and variables yielded the best
  predictive results. However\, these studies do not tell a manager of a ye
 t-to-commence panel survey which technique is best for their own context (
 e.g.\, annual vs. quarterly waves\, household vs. individual sampling). St
 udies have shown mixed results regarding the performance of nonresponse pr
 ediction in different panel contexts. In addition\, there is considerable 
 variation in which prediction technique (e.g.\, algorithm and variables) p
 erforms best across survey settings. It is thus unclear under which condit
 ions predictive models successfully identify nonresponders and which techn
 iques are best suited to which contexts. \n\nTo address the question of cr
 oss-panel generalizability\, we compare machine learning-based nonresponse
  prediction across five panel surveys of the general German population: th
 e Socio-Economic Panel (SOEP)\, the German Internet Panel (GIP)\, the GESI
 S Panel\, the Mannheim Corona Stud (MCS)\, and the Family Demographic Pane
 l (FREDA). We evaluate how differences in the design of the surveys and di
 fferences in the sample composition (e.g.\, average sample age and income)
  impact the characteristics of the best-performing machine learning model 
 (e.g.\, the best algorithm\, accuracy scores\, and the most predictive var
 iables). We compare which (types of) variables and algorithms are the most
  predictive across these contexts. We also evaluate how well techniques fr
 om one survey transfer to a different survey context. Our analysis shows t
 he extent to which practitioners can expect the modeling techniques of one
  survey to generalize to their own context and the factors which might inh
 ibit generalizability.
DTSTAMP:20260716T071506Z
LOCATION:Sutherland Room
SUMMARY:Evaluating the cross-panel transferability of machine learning mode
 ls for predicting panel nonresponse. - John Collins
URL:https://conferences.acspri.org.au/2024/talk/UHGAYZ/
END:VEVENT
END:VCALENDAR