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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20181120T134654Z
DTSTART;VALUE=DATE:20190314
DTEND;VALUE=DATE:20190316
SUMMARY:Introduction to Latent Class Analysis
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}m5o-jopsm7s
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DESCRIPTION:Latent Class Analysis (LCA) is a branch of the more General L
atent Variable Modelling approach. It is typically used to classify subj
ects (such as individuals or countries) in groups that represent underly
ing patterns from the data. In addition to this application LCA provides
a flexible framework that can be used in a wide range of contexts: in l
ongitudinal studies (e.g.\, mixture latent growth models\, hidden Markov
chains)\, in evaluation of data quality (e.g.\, extreme response style\
, cross-cultural equivalence)\, non-parametric multilevel models\, joint
modelling for dealing with missing data.\n\nIn this course you will rec
eive an introduction to the essential topics of LCA such as: what is LCA
\, how to run models\, how to choose between alternative models\, how to
classify observations\, how to evaluate and predict classifications. Yo
u will also apply this knowledge to a number of more advanced models tha
t look at the relationship between latent class variables and at longitu
dinal data.\n\nThe course covers:\nRefresher of basic concepts in catego
rical analysis: (marginal) probability\, odds ratios\, logistic regressi
on\;\nBasic concepts and assumptions of latent class analysis\;\nIntrodu
ction to Latent GOLD software\;\nModel fit evaluation: global\, local an
d substantive evaluation\;\nClassification of cases\;\nApply these conce
pts to a number of models looking at: predicting class membership\, rela
tionships between latent classes\, hidden Markov chains.\nBy the end of
the course participants will:\n\nKnow what is Latent Class Analysis\;\nB
e able to estimate and interpret results from Latent Class Analysis\;\nB
e able to choose between alternative Latent Class Models\;\nUnderstand l
atent class classification and how to predict it\;\nBe able to investiga
te the relationship between latent class variables.\nPre-requisites\n\nK
nowledge of basic categorical analysis: (marginal) probabilities\, odds
ratios\, logistic regression.\n\nDay 1 – introduction to LCA\n\nRefreshe
r of basic concepts in categorical analysis: (marginal) probability\, od
ds ratios\, logistic regression\;\nBasic concepts and assumptions of lat
ent class analysis\;\nIntroduction to Latent GOLD software\;\nModel fit
evaluation: global\, local and substantive evaluation\;\nClassification
of observations.\n\nDay 2 – applications of LCA\n\nPredicting class memb
ership\,\nModelling multiple latent classes\;\nLooking at relationships
between latent class variables\;\nHidden Markov chains.\n\nEach day will
run from 10am – 4:30 (approx.)
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Basement Lab\, Humanities Bridgeford Street\, Manchester
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