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DTSTART:19700101T000000
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DTSTAMP:20210303T190556Z
SUMMARY:Longitudinal Data Analysis Using Structural Equation Modeling
DESCRIPTION:Taught by Paul Allison, Ph.D. \nFor the past eight yea
rs, Professor Paul Allison has been teaching his acclaimed two-day sem
inar “Longitudinal Data Analysis Using Stata”. In this new semin
ar he takes up where that course leaves off, with methods for analyzin
g panel data using structural equation modeling.\nPanel data have two
big attractions for making causal inferences with non-experimental dat
a:\n\nThe ability to control for unobserved, time-invariant confounder
s.\nThe ability to determine the direction of causal relationships.\n\
nFixed effects methods that control for unobserved confounders are now
well known and widely used. Cross-lagged panel models have long been
used to study the direction of causality. But trying to combine these
two approaches poses serious problems.\nTo deal with these difficultie
s, econometricians have developed the Arellano-Bond methods for estima
ting dynamic panel data models. However, Arellano-Bond is known to b
e statistically inefficient and can also be severely biased in importa
nt situations.\nProfessor Allison has recently shown that dynamic pane
l models can easily be estimated by maximum likelihood with SEM softwa
re (ML-SEM). His recent work demonstrates that the performance of ML
-SEM is superior to the econometric methods: In typical situations, th
e use of Arellano-Bond instead of ML-SEM is equivalent to throwing awa
y half the data. Plus, ML-SEM offers better capabilities for handling
missing data, evaluating model fit, relaxing constraints, and allowin
g for non-normal data.\nThis seminar takes a deep dive into the ML-SEM
method for estimating dynamic panel models, exploring the ins and out
s of assumptions, model specification, software programming, model eva
luation and interpretation of results. Several real data sets are anal
yzed in great detail, testing out alternative methods and working towa
rd an optimal solution. Both the –sem- and the –gsem- commands wil
l be explored. In addition, Professor Allison will explain his new –
xtdpdml- command which radically reduces the programming necessary to
run the panel data models.\nThis is an applied course with a minimal n
umber of formulas and a maximal number of examples. Although the metho
dology is cutting edge, the emphasis is on how to actually do the anal
ysis in order accomplish your objectives.\nAt the end of this seminar,
you should be able to confidently apply the ML-SEM method for dynamic
panel data to your own research projects. You will also have a thorou
gh understanding of the rationale, assumptions, and interpretation of
these methods. Note: the methods covered in this course require panel
data with at least three time points, and the number of individuals sh
ould be substantially larger than the number of time points.\n\nFor mo
re information visit https://www.datasciencecentral.com/events/longitu
dinal-data-analysis-using-structural-equation-modeling
DTSTART;TZID=Pacific/Gambier:20160225T090000
DTEND;TZID=Pacific/Gambier:20160226T160000
CATEGORIES:training, seminar
LOCATION:Hotel Birger Jarl Conference
WEBSITE:http://statisticalhorizons.com/seminars/public-seminars/sweden
-course-series-longitudinal-data-analysis-using-structural-equation-mo
deling
URL:http://statisticalhorizons.com/seminars/public-seminars/sweden-cou
rse-series-longitudinal-data-analysis-using-structural-equation-modeli
ng
CONTACT:610-642-1941
ORGANIZER;CN="Statistical Horizons LLC":https://www.datasciencecentral
.com/profile/MaryKiely
ATTACH;FMTTYPE="image/jpeg":
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=ACCEPTED;RSVP=TRUE;CN="Statisti
cal Horizons LLC":https://www.datasciencecentral.com/profile/MaryKiely
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