Introductory And Intermediate Growth Models - Mplus

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Maximum-Likelihood AlternativesNote that [y, x] = [y | x] * [x], where the marginal distribution [x]is unrestricted.Normal theory ML for• [y, x]: Gives the same results as [y | x] when there is nomissing data (Joreskog & Goldberger, 1975). Typically used inSEM– With missing data on x, the normality assumption for x isan additional assumption than when using [y | x]• [y | x]: Makes normality assumptions for residuals, not forx. Typically used outside SEM– Used with Type = Random, Type = Mixture, and withcategorical, censored, and count outcomes– Deletes individuals with missing on any x• [y, x] versus [y | x] gives different sample sizes and thelikelihood and BIC values are not on a comparable scale135Alternative <strong>Models</strong>With Time-Varying Covariates13668
Alternative <strong>Models</strong>With Time-Varying CovariatesModel M1Model M2math7 math8 math9 math10math7 math8 math9 math10iissstvcfemalefemalemothedmothedhomerescrs7crs8 crs9 crs10homerescrs7crs8 crs9 crs10137Input Excerpts Model M1ANALYSIS:TYPE = RANDOM; ! gives loglikelihood in [y | x] metricMODEL:i s | math7@0 math8@1 math9@2 math10@3;i s ON female mothed homeres;math7 ON mthcrs7;math8 ON mthcrs8;math9 ON mthcrs9;math10 ON mthcrs10;13869
Page 1 and 2:
Mplus Short CoursesTopic 3Growth Mo
Page 3 and 4:
Statistical Analysis With Latent Va
Page 5 and 6:
General Latent Variable Modeling Fr
Page 7 and 8:
Overview Of Mplus Courses (Continue
Page 9 and 10:
Mean CurveMath Achievement40 50 60
Page 11 and 12:
Loneliness In TwinsAge range: 13-85
Page 13 and 14:
Individual Development Over Time(1)
Page 15 and 16:
Comparison Summary Of Multilevel,Mi
Page 17 and 18: Random Effects: SEM AndMultilevel M
Page 19 and 20: Multilevel Modeling In ALatent Vari
Page 21 and 22: Individual Development Over Timeyi
Page 23 and 24: Interpretation Of The Linear Growth
Page 25 and 26: Latent Growth Model ParametersFor F
Page 27 and 28: Growth Model Covariancesy ti= η 0i
Page 29 and 30: CFA Modeling Estimation And Testing
Page 31 and 32: Alternative Growth Model Parameteri
Page 33 and 34: LSAY Math AchievementMean CurveMath
Page 35 and 36: TITLE:DATA:VARIABLE:MODEL:Input For
Page 37 and 38: Output Excerpts LSAY Linear GrowthM
Page 39 and 40: Output Excerpts LSAY Linear Growth
Page 41 and 42: Output Excerpts LSAY:Adding Correla
Page 43 and 44: Time-Invariant AndTime-Varying Cova
Page 45 and 46: Output Excerpts LSAY Growth ModelWi
Page 47 and 48: Output Excerpts LSAY Growth ModelWi
Page 50 and 51: Centering• Centering determines t
Page 52 and 53: Further Readings On Introductory Gr
Page 54 and 55: Growth Model With Free Time Scores1
Page 56 and 57: Input Excerpts For LSAYLinear Growt
Page 58 and 59: Quadratic growth modelSpecifying Ti
Page 60 and 61: Piecewise Growth Modeling• Can be
Page 62 and 63: Output Excerpts LSAY Piecewise Grow
Page 64 and 65: Growth Modeling In Multilevel Terms
Page 66 and 67: Output Excerpts For Growth Model Wi
Page 70 and 71: Output Excerpts Model M1TESTS OF MO
Page 72 and 73: 72143Output Excerpts Model M1 (Cont
Page 74 and 75: Output Excerpts Model M1 (Continued
Page 76 and 77: Input Excerpt Model M3ANALYSIS:TYPE
Page 78 and 79: Time-Varying CovariatesRepresenting
Page 80 and 81: Regressions Among Random Effects159
Page 82 and 83: Output Excerpts Growth Model With A
Page 84 and 85: Interpreting The Effect Of The Inte
Page 86 and 87: Multiple Processes• Parallel proc
Page 88 and 89: math7 math8 math9math10imsmmothedia
Page 90 and 91: Output Excerpts LSAY ParallelProces
Page 92 and 93: Categorical Outcomes: Logit And Pro
Page 94 and 95: Logistic Regression And Log Odds (C
Page 96 and 97: Growth Models With Categorical Outc
Page 98 and 99: Input For Schizophrenia Data Growth
Page 100 and 101: Output Excerpts Schizophrenia Data
Page 106 and 107: References (Continued)Jennrich, R.I
Page 108 and 109: References (Continued)Ferrer, E. &
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Introductory And Intermediate Growth Models - Mplus

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