Advances in Longitudinal Data Analysis - Society of Behavioral ...

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Latent class analysis = categorical latent variableobserved categorical variablesResponses Joe Jane Bob BettyAny smoking Yes Yes No NoAny smoking“CLASS”Cigarettes perdayStage ofchangeE E ECigarettes perday = 0No No Yes YesCigarettes perday
Latent class analysis = categorical latent variableobserved categorical variablesClass 1= non smoking, action stage (10%)Class 2 = smoking, 12-20 CPD, preparation stage (13%)Class 3 = smoking, 20+ CPD, preparation stage (77%)Any smoking“CLASS”Cigarettes perdayStage ofchangeE E EProbability ofendorsingC1 C2 C3Any smoking 0.00 1.00 1.000 CPD 1.00 0.00 0.0012-20 CPD 0.00 1.00 0.3420+ CPD 0.00 0.00 0.38Contemplation 0.00 0.00 0.07Preparation 0.11 0.99 0.93Action 0.89 0.01 0.0025
Page 1 and 2: Katie Witkiewitz, PhDDepartment of
Page 3: What will not be covered?Level 1: y
Page 6 and 7: Question 1: How doindividuals chang
Page 8 and 9: Question 2: Does the numberof cigar
Page 10 and 11: Question 3: Are therechanges in qui
Page 12 and 13: Question 4: How do weexamine the ef
Page 14: Examples of Longitudinal Mixture Mo
Page 17 and 18: YYLatent variables can be continuou
Page 19 and 20: Finite Mixture Modeling19
Page 21 and 22: latent profile analysislatent class
Page 23: Latent profile analysis = categoric
Page 27 and 28: yLatent Growth Curve = longitudinal
Page 29 and 30: Latent Growth Mixture Model: Combin
Page 31 and 32: Latent Class Growth Analysis (i.e.,
Page 33 and 34: Latent Markov model = estimate prob
Page 35 and 36: Latent Markov model = estimate prob
Page 37 and 38: Latent transition analysis = estima
Page 39 and 40: Latent transition analysisProbabili
Page 41: Repeated measures latent classanaly
Page 44 and 45: Check your distributionsWitkiewitz
Page 46 and 47: Time, it’s on your side… # of t
Page 48 and 49: Class Enumeration???48
Page 50 and 51: Classification precision Model entr
Page 52 and 53: Example 1: Observed data…(n = 395
Page 54 and 55: Three Class Growth Mixture Model: %
Page 56 and 57: Type of mixture model???56
Page 58 and 59: Comparison of latent growth mixture
Page 60 and 61: Growth mixture model:“Infrequent
Page 62 and 63: Growth mixture model:“Frequent he
Page 64 and 65: Latent Markov model:“Infrequent t
Page 66 and 67: Cross-classification of models66
Page 69 and 70: Common research questionsDo differe
Page 71 and 72: Growth Mixture Model with Covariate
Page 73 and 74: Covariates predicting within class
Page 75 and 76:
Predict 12-month point prevalenceEq
Page 77:
Latent transition analysis with tim
Page 80 and 81:
Summary of modeling issues Over-ext
Page 83 and 84:
Software Mplus - www.statmodel.com
Page 85 and 86:
Screen shot of LGMMcommands85
Page 87 and 88:
FINAL CLASS COUNTS AND PROPORTIONS
Page 89 and 90:
MODEL RESULTSTwo-TailedEstimate S.E
Page 91 and 92:
Estimate S.E. Est./S.E. P-ValueLate
Page 93 and 94:
Screen shot of LMM commands93
Page 95 and 96:
LATENT TRANSITION PROBABILITIES BAS
Page 97 and 98:
Latent transition analysiscontinued
Page 99 and 100:
Screen shot of RMLCAcommands99
Page 101 and 102:
Covariates in MplusDo different lev
Page 103 and 104:
Common Mplus error messagesErrorWAR
Page 106 and 107:
Advanced longitudinal mixture model
Page 108 and 109:
COMMAND LINESTITLE:DATA:VARIABLE:DE
Page 110 and 111:
VARIABLE COMMANDNAMES AREnames of v
Page 112 and 113:
MODEL COMMANDPART OF THE MODEL%OVER
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