Introductory And Intermediate Growth Models - Mplus

More documents

Recommendations

Info

Output Excerpts LSAY:Adding Correlated Residuals (Continued)VariancesEstimatesS.E.Est./S.E.Two-tailedP-valueI92.0384.16722.0850.000S3.0430.7893.8580.000Residual VariancesMATH711.8713.4663.4250.001MATH814.0271.9807.0850.000MATH932.5962.60912.4920.000MATH1033.8574.8157.0320.00079Output Excerpts LSAY:Adding Correlated Residuals (Continued)ESTIMATED MODEL AND RESIDUALS (OBSERVED – ESTIMATED)Model Estimated Means/Intercepts/ThresholdsMATH7MATH8MATH9MATH10150.203 54.140 58.07662.012Residuals for Means/Intercepts/ThresholdsMATH7MATH8MATH9MATH1010.153 -0.267 -0.1140.238Standardized Residuals (z-scores) forMeans/Intercepts/ThresholdsMATH7MATH8MATH9MATH1014.198 -4.109 -1.2565.199Normalized Residuals for Means/Intercepts/ThresholdsMATH7MATH8MATH9MATH1010.834-1.317-0.4780.9048040
Output Excerpts LSAY:Adding Correlated Residuals (Continued)Model Estimated Covariances/Correlations/ResidualCorrelationsMATH7MATH8MATH9MATH7MATH8MATH9MATH10MATH7103.91093.093104.304-0.0410.0020.024-0.434121.375121.441MATH10110.437 125.700 158.025 190.083Residuals for Covariances/Correlations/Residual CorrelationsMATH7MATH8MATH8-0.081-0.002-0.345MATH9161.339MATH90.055-0.368MATH10MATH10-0.98781Output Excerpts LSAY:Adding Correlated Residuals (Continued)Standardized Residuals (z-scores) forCovariances/Correlations/Residual CorrMATH7MATH8MATH9MATH10Normalized Residuals for Covariances/Correlations/ResidualCorrelationsMATH7MATH8MATH9 MATH10MATH7MATH8MATH9MATH10MATH7999.000999.0000.279999.000-0.0160.0010.008-0.130MATH8999.000999.000999.000-0.025-0.001-0.092MATH90.297999.0000.012-0.081MATH10999.000-0.1858241
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: Output Excerpts LSAY Linear Growth
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 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 102 and 103:
Page 104 and 105:
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?