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DTSTAMP:20130518T175916Z
SUMMARY:Introduction to Structural Equation Modeling
DESCRIPTION:Structural Equation Modeling (SEM) is a statistical method
 ology that is widely used by researchers in the social, behavioral and
  educational sciences.  First introduced in the 1970s, SEM was a marr
 iage of psychometrics and econometrics. On the psychometric side, SEM 
 allows for latent variables with multiple indicators. On the economet
 ric side, SEM allows for multiple equations, possibly with feedback l
 oops. In today's SEM software, the models are so general that they enc
 ompass most of the statistical methods that are currently used in the
  social and behavioral sciences.  \nHere Are a Few Things You Can Do 
 With Structural Equation Models\n    • Test complex causal theo
 ries with multiple pathways.     • Estimate simultaneous equati
 ons with reciprocal effects.     • Incorporate latent variables
  with multiple indicators.     • Investigate mediation and mode
 ration in a systematic way.     • Handle missing data by maximu
 m likelihood (better than multiple imputation).     • Analyze l
 ongitudinal data.      • Estimate fixed and random effects mod
 els in a comprehensive framework.     • Adjust for measurement 
 error in predictor variables.  Because SEM is such a complex and wide-
 ranging methodology, learning how to use it can take a substantial inv
 estment of time and effort. Now, you have a unique opportunity to lear
 n the basics of SEM from a master teacher, Professor Paul D. Allison, 
 in just two days.\n\nFor more information visit http://www.datascience
 central.com/events/introduction-to-structural-equation-modeling
DTSTART;TZID=Pacific/Gambier:20120517T000000
DTEND;TZID=Pacific/Gambier:20120518T235900
CATEGORIES:data, analysis
LOCATION:Jamaica Bay Inn
WEBSITE:https://www.regonline.com/sem_la_2012
URL:https://www.regonline.com/sem_la_2012
CONTACT:610-642-1941
ORGANIZER:Statistical Horizons LLC
ATTACH;FMTTYPE="image/jpeg":
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=ACCEPTED;RSVP=TRUE;CN="Mary Kie
 ly":http://www.datasciencecentral.com/profile/MaryKiely
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