Introductory And Intermediate Growth Models - Mplus

More documents

Recommendations

Info

Time-Varying CovariatesRepresenting Status Changeyx155Marital Status Change And Alcohol Use(Curran, Muthen, & Harford, 1998).51 .48 .50 .41Time 1Alcohol UseTime 2Alcohol UseTime 3Alcohol UseTime 4Alcohol UseAge1.01.0 1.0Gender.31-.21AlcoholUseIntercept1.01.0 1.01.0Black.23.14.841.0 1.5* 2.5* 1.01.01.0 1.0 1.0 1.0Hispanic.11.08AlcoholUseSlopeTime 1IncrementalChangeTime 2IncrementalChangeTime 3IncrementalChangeTime 4IncrementalChangeEducation.96x 1 x 2 x-.29 -.28 -.20 -.20Time 1 Time 2 Time 3 Time 4Status Status Status Status315678
Computational Issues For Growth Models• Decreasing variances of the observed variables over time may make themodeling more difficult• Scale of observed variables – keep on a similar scale• Convergence – often related to starting values or the type of model beingestimated• Program stops because maximum number of iterations has been reached• If no negative residual variances, either increase the number ofiterations or use the preliminary parameter estimates as starting values• If there are large negative residual variances, try better starting values• Program stops before the maximum number of iterations has beenreached• Check if variables are on a similar scale• Try new starting values• Starting values – the most important parameters to give starting values to areresidual variances and the intercept growth factor mean• Convergence for models using the | symbol• Non-convergence may be caused by zero random slope variances whichindicates that the slopes should be fixed rather than random157Advantages Of Growth ModelingIn A Latent Variable Framework• Flexible curve shape• Individually-varying times of observation• Regressions among random effects• Multiple processes• Modeling of zeroes• Multiple populations• Multiple indicators• Embedded growth models• Categorical latent variables: growth mixtures15879
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 68 and 69: Maximum-Likelihood AlternativesNote
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 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. &
show all

Introductory And Intermediate Growth Models - Mplus

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?