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 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
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
 q-7jy55b
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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