BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN
BEGIN:VTIMEZONE
TZID:Central Standard Time
BEGIN:STANDARD
DTSTART:20251102T020000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYHOUR=2;BYMINUTE=0;BYMONTH=11
TZNAME:Central Standard Time
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20250301T020000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYHOUR=2;BYMINUTE=0;BYMONTH=3
TZNAME:Central Daylight Time
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
DESCRIPTION:When fitting models to data\, general assumptions are frequentl
 y made automatically without much consideration for their implication on s
 ubsequent interpretations. For instance\, fitting a standard factor model 
 often presupposes an underlying set of continuous\, latent factors. Likewi
 se\, when searching for group structure\, mixture models (e.g.\, latent pr
 ofile analysis\, latent class analysis) or cluster analysis are implemente
 d and assume a set of discrete latent classes". Usually\, the type of mode
 l that is fit to the data is governed by the theoretical notions underpinn
 ing the substantive question of interest. In this talk\, it is shown that 
 both types of structures can be present and correspond to different subset
 s of the data. A general strategy is discussed for extracting both class s
 tructure and factor structure. Demonstrations are given on a data set of i
 nternet habits of collegiate students.
DTEND:20150427T180000Z
DTSTAMP:20260612T232306Z
DTSTART:20150427T170000Z
LOCATION:USA
SEQUENCE:0
SUMMARY:Steinley Talk
UID:RFCALITEM639168853860246809
X-ALT-DESC;FMTTYPE=text/html:<p>When fitting models to data\, general assum
 ptions are frequently made automatically without much consideration for th
 eir implication on subsequent interpretations. For instance\, fitting a st
 andard factor model often presupposes an underlying set of continuous\, la
 tent factors. Likewise\, when searching for group structure\, mixture mode
 ls (e.g.\, latent profile analysis\, latent class analysis) or cluster ana
 lysis are implemented and assume a set of discrete latent classes". Usuall
 y\, the type of model that is fit to the data is governed by the theoretic
 al notions underpinning the substantive question of interest. In this talk
 \, it is shown that both types of structures can be present and correspond
  to different subsets of the data. A general strategy is discussed for ext
 racting both class structure and factor structure. Demonstrations are give
 n on a data set of internet habits of collegiate students.</p>
END:VEVENT
END:VCALENDAR