# The Latent Variable Growth Model In Practice. Individual Development Over Time

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1 The Latent Variable Growth Model In Practice 37 Individual Development Over Time y i = 1 i = 2 i = 3 t = 1 t = 2 t = 3 t = 4 ε 1 ε 2 ε 3 ε 4 y 1 y 2 y 3 y 4 x η 0 η 1 (1) y ti = η 0i + η 1i x t + ε ti (2a) (2b) η 0i = α 0 + γ 0 w i + ζ 0i η 1i = α 1 + γ 1 w i + ζ 1i w 38 19

2 Linear Growth Model Specifying Time Scores For Linear Growth Models Need two latent variables to describe a linear growth model: Intercept and slope Outcome Time Equidistant time scores for slope: Specifying Time Scores For Linear Growth Models (Continued) Outcome Time Nonequidistant time scores for slope:

3 Interpretation Of The Linear Growth Factors Model: y ti = η 0i + η 1i x t + ε ti, (17) where in the example t = 1, 2, 3, 4 and x t = 0, 1, 2, 3: y 1i = η 0i + η 1i 0 + ε 1i, (18) η 0i = y 1i ε 1i, (19) y 2i = η 0i + η 1i 1 + ε 2i, (20) y 3i = η 0i + η 1i 2 + ε 3i, (21) y 4i = η 0i + η 1i 3 + ε 4i. (22) 41 Interpretation Of The Linear Growth Factors (Continued) Interpretation of the intercept growth factor η 0i (initial status, level): Systematic part of the variation in the outcome variable at the time point where the time score is zero. Unit factor loadings Interpretation of the slope growth factor η 1i (growth rate, trend): Systematic part of the increase in the outcome variable for a time score increase of one unit. Time scores determined by the growth curve shape 42 21

4 Interpreting Growth Model Parameters Intercept Growth Factor Parameters Mean Average of the outcome over individuals at the timepoint with the time score of zero; When the first time score is zero, it is the intercept of the average growth curve, also called initial status Variance Variance of the outcome over individuals at the timepoint with the time score of zero, excluding the residual variance 43 Interpreting Growth Model Parameters (Continued) Linear Slope Growth Factor Parameters Mean average growth rate over individuals Variance variance of the growth rate over individuals Covariance with Intercept relationship between individual intercept and slope values Outcome Parameters Intercepts not estimated in the growth model fixed at zero to represent measurement invariance Residual Variances time-specific and measurement error variation Residual Covariances relationships between timespecific and measurement error sources of variation across time 44 22

5 Latent Growth Model Parameters And Sources Of Model Misfit ε 1 ε 2 ε 3 ε 4 y 1 y 2 y 3 y 4 η 0 η 1 45 Latent Growth Model Parameters For Four Time Points Linear growth over four time points, no covariates. Free parameters in the H 1 unrestricted model: 4 means and 10 variances-covariances Free parameters in the H 0 growth model: (9 parameters, 5 d.f.): Means of intercept and slope growth factors Variances of intercept and slope growth factors Covariance of intercept and slope growth factors Residual variances for outcomes Fixed parameters in the H 0 growth model: Intercepts of outcomes at zero Loadings for intercept growth factor at one Loadings for slope growth factor at time scores Residual covariances for outcomes at zero 46 23

6 Latent Growth Model Sources Of Misfit Sources of misfit: Time scores for slope growth factor Residual covariances for outcomes Outcome variable intercepts Loadings for intercept growth factor Model modifications: Recommended Time scores for slope growth factor Residual covariances for outcomes Not recommended Outcome variable intercepts Loadings for intercept growth factor 47 Latent Growth Model Parameters For Three Time Points Linear growth over three time points, no covariates. Free parameters in the H 1 unrestricted model: 3 means and 6 variances-covariances Free parameters in the H 0 growth model (8 parameters, 1 d.f.) Means of intercept and slope growth factors Variances of intercept and slope growth factors Covariance of intercept and slope growth factors Residual variances for outcomes Fixed parameters in the H 0 growth model: Intercepts of outcomes at zero Loadings for intercept growth factor at one Loadings for slope growth factor at time scores Residual covariances for outcomes at zero 48 24

7 Growth Model Means And Variances y ti = η 0i + η 1i x t + ε ti, x t = 0, 1,, T 1. Expectation (mean; E) and variance (V): E (y ti ) = E (η 0i ) + E (η 1i ) x t, 2 V (y ti ) = V (η 0i ) + V (η 1i ) x t + 2x t Cov (η 0i, η 1i ) + V (ε ti ) E(y ti ) V(y ti ) } E(η0i) t } V(η0i) V(ε ti ) constant over t Cov(η 0, η 1 ) = 0 t 49 } E(η 1i ) } V(η 1i ) Growth Model Covariances y ti = η 0i + η 1i x t + ε ti, x t = 0, 1,, T 1. Cov(y ti,y t í ) = V(η 0i ) + V(η 1i ) x t x t + Cov(η 0i, η 1i ) (x t + x t ) + Cov(ε ti, ε t i ). V(η 0t ) : V(η 1i ) x t x t : t t t t η 0 η 1 η 0 η 1 Cov(η 0i, η 1i ) x t : Cov(η 0i, η 1i ) x t : t t t t η 0 η 1 η 0 η

8 Growth Model Estimation, Testing, And Model Modification Estimation: Model parameters Maximum-likelihood (ML) estimation under normality ML and non-normality robust s.e. s Quasi-ML (MUML): clustered data (multilevel) WLS: categorical outcomes ML-EM: missing data, mixtures Model Testing Likelihood-ratio chi-square testing; robust chi square Root mean square of approximation (RMSEA): Close fit (.05) Model Modification Expected drop in chi-square, EPC Estimation: Individual growth factor values (factor scores) Regression method Bayes modal Empirical Bayes Factor determinacy 51 Alternative Growth Model Parameterizations Parameterization 1 for continuous outcomes y ti = 0 + η 0i + η 1i x t + ε ti, (32) η 0i = α 0 + ζ 0i, (33) η 1i = α 1 + ζ 1i. (34) Parameterization 2 for categorical outcomes and multiple indicators y ti = v + η 0i + η 1i x t + ε ti, (35) η 0i = 0 + ζ 0i, (36) η 1i = α 1 + ζ 1i. (37) 52 26

9 Alternative Growth Model Parameterizations Parameterization 1 for continuous outcomes Outcome variable intercepts fixed at zero Growth factor means free to be estimated MODEL: i BY s BY i s]; Parameterization 2 for categorical outcomes and multiple indicators Outcome variable intercepts constrained to be equal Intercept growth factor mean fixed at zero MODEL: i BY s BY [y1-y4] (1); s]; 53 Simple Examples Of Growth Modeling 54 27

10 Steps In Growth Modeling Preliminary descriptive studies of the data: means, variances, correlations, univariate and bivariate distributions, outliers, etc. Determine the shape of the growth curve from theory and/or data Individual plots Mean plot Consider change in variance across time Fit model without covariates using fixed time scores Modify model as needed Add covariates 55 LSAY Data The data come from the Longitudinal Study of American Youth (LSAY). Two cohorts were measured at four time points beginning in Cohort 1 was measured in Grades 10, 11, and 12. Cohort 2 was measured in Grades 7, 8, 9, and 10. Each cohort contains approximately 60 schools with approximately 60 students per school. The variables measured include math and science achievement items, math and science attitude measures, and background information from parents, teachers, and school principals. There are approximately 60 items per test with partial item overlap across grades adaptive tests. Data for the analysis include the younger females. The variables include math achievement from Grades 7, 8, 9, and 10 and the background variables of mother s education and home resources

11 Mean Curve Math Achievement Grade Individual Curves Math Achievement Grade 57 Input For LSAY TYPE=BASIC Analysis TITLE: DATA: VARIABLE: ANALYSIS: PLOT: LSAY For Younger Females With Listwise Deletion TYPE=BASIC Analysis FILE IS lsay.dat; FORMAT IS 3F8.0 F8.4 8F8.2 3F8.0; NAMES ARE cohort id school weight math7 math8 math9 math10 att7 att8 att9 att10 gender mothed homeres; USEOBS = (gender EQ 1 AND cohort EQ 2); MISSING = ALL (999); USEVAR = math7-math10; TYPE = BASIC; TYPE = PLOT1; 58 29

12 Sample Statistics For LSAY Data Means Covariances n = Correlations math7 math8 math9 math10 i s 60 30

13 Input For LSAY Linear Growth Model Without Covariates TITLE: DATA: VARIABLE: ANALYSIS: MODEL: OUTPUT: LSAY For Younger Females With Listwise Deletion Linear Growth Model Without Covariates FILE IS lsay.dat; FORMAT IS 3F8.0 F8.4 8F8.2 3F8.0; NAMES ARE cohort id school weight math7 math8 math9 math10 att7 att8 att9 att10 gender mothed homeres; USEOBS = (gender EQ 1 AND cohort EQ 2); MISSING = ALL (999); USEVAR = math7-math10; TYPE = MEANSTRUCTURE; i BY s BY [i s]; SAMPSTAT STANDARDIZED MODINDICES (3.84); Alternative language: MODEL: i s 61 Output Excerpts LSAY Linear Growth Model Without Covariates Tests Of Model Fit Chi-Square Test of Model Fit Value Degrees of Freedom 5 P-Value 04 CFI/TLI CFI TLI RMSEA (Root Mean Square Error Of Approximation) Estimate Percent C.I Probability RMSEA <= SRMR (Standardized Root Mean Square Residual) Value

14 Output Excerpts LSAY Linear Growth Model Without Covariates (Continued) Modification Indices M.I. E.P.C. Std.E.P.C. StdYX E.P.C. S BY S BY S BY Output Excerpts LSAY Linear Growth Model Without Covariates (Continued) Model Results I BY S BY Estimates S.E. Est./S.E. Std StdYX

15 Output Excerpts LSAY Linear Growth Model Without Covariates (Continued) Means I S Intercepts Estimates S.E Est./S.E Std StdYX Output Excerpts LSAY Linear Growth Model Without Covariates (Continued) I WITH S Residual Variances Variances I S R-Square Observed Variable Estimates R-Square S.E Est./S.E Std StdYX

16 Growth Model With Free Time Scores 67 Specifying Time Scores For Non-Linear Growth Models With Estimated Time Scores Non-linear growth models with estimated time scores Need two latent variables to describe a non-linear growth model: Intercept and slope Outcome Outcome Time Time Time scores: 0 1 Estimated Estimated 68 34

17 Interpretation Of Slope Growth Factor Mean For Non-Linear Models The slope growth factor mean is the change in the outcome variable for a one unit change in the time score In non-linear growth models, the time scores should be chosen so that a one unit change occurs between timepoints of substantive interest. An example of 4 timepoints representing grades 7, 8, 9, and 10 Time scores of 0 1 * * slope factor mean refers to change between grades 7 and 8 Time scores of 0 * * 1 slope factor mean refers to change between grades 7 and Growth Model With Free Time Scores Identification of the model for a model with two growth factors, at least one time score must be fixed to a non-zero value (usually one) in addition to the time score that is fixed at zero (centering point) Interpretation cannot interpret the mean of the slope growth factor as a constant rate of change over all timepoints, but as the rate of change for a time score change of one. Approach fix the time score following the centering point at one Choice of time score starting values if needed Means Differences Time scores 0 1 >2 >

18 Input Excerpts For LSAY Linear Growth Model With Free Time Scores Without Covariates MODEL: OUTPUT: i s math9 math10; RESIDUAL; Alternative language: MODEL: i BY s BY math9 math10; [i s]; 71 Output Excerpts LSAY Growth Model With Free Time Scores Without Covariates Tests Of Model Fit n = 984 Chi-Square Test of Model Fit Value Degrees of Freedom P-Value CFI/TLI CFI TLI RMSEA (Root Mean Square Error Of Approximation) Estimate 90 Percent C.I. Probability RMSEA <=.05 SRMR (Standardized Root Mean Square Residual) Value

19 Output Excerpts LSAY Growth Model With Free Time Scores Without Covariates (Continued) Selected Estimates I S Estimates S.E. Est./S.E. Std StdYX Output Excerpts LSAY Growth Model With Free Time Scores Without Covariates (Continued) Estimates S.E. Est./S.E. Std StdYX S I WITH Variances I S Means I S

20 Output Excerpts LSAY Growth Model With Free Time Scores Without Covariates (Continued) Residuals Model Estimated Means/Intercepts/Thresholds Residuals for Means/Intercepts/Thresholds Output Excerpts LSAY Growth Model With Free Time Scores Without Covariates (Continued) Model Estimated Covariances/Correlations/Residual Correlations Residuals for Covariances/Correlations/Residual Correlations

21 Covariates In The Growth Model 77 Covariates In The Growth Model Types of covariates Time-invariant covariates vary across individuals not time, explain the variation in the growth factors Time-varying covariates vary across individuals and time, explain the variation in the outcomes beyond the growth factors 78 39

22 Time-Invariant And Time-Varying Covariates y 1 y 2 y 3 y 4 η 0 η 1 w a 21 a 22 a 23 a LSAY Growth Model With Time-Invariant Covariates math7 math8 math9 math10 i s mothed homeres 80 40

23 Input Excerpts For LSAY Linear Growth Model With Free Time Scores And Covariates VARIABLE: ANALYSIS: MODEL: NAMES ARE cohort id school weight math7 math8 math9 math10 att7 att8 att9 att10 gender mothed homeres; USEOBS = (gender EQ 1 AND cohort EQ 2); MISSING = ALL (999); USEVAR = math7-math10 mothed homeres;!estimator = MLM; i s math9 math10; i s ON mothed homeres; Alternative language: MODEL: i BY s BY math9 math10; [i s]; i s ON mothed homeres; 81 Output Excerpts LSAY Growth Model With Free Time Scores And Covariates Tests Of Model Fit for ML n = 935 Chi-Square Test of Model Fit Value Degrees of Freedom P-Value CFI/TLI CFI TLI RMSEA (Root Mean Square Error Of Approximation) Estimate 90 Percent C.I. Probability RMSEA <=.05 SRMR (Standardized Root Mean Square Residual) Value

24 Output Excerpts LSAY Growth Model With Free Time Scores And Covariates (Continued) Tests Of Model Fit for MLM Chi-Square Test of Model Fit Value Degrees of Freedom P-Value Scaling Correction Factor for MLM CFI/TLI CFI TLI RMSEA (Root Mean Square Error Of Approximation) Estimate SRMR (Standardized Root Mean Square Residual) Value WRMR (Weighted Root Mean Square Residual) Value * Output Excerpts LSAY Growth Model With Free Time Scores And Covariates (Continued) Selected Estimates For ML Estimates S.E. Est./S.E. Std StdYX I ON MOTHED HOMERES S ON MOTHED HOMERES I WITH S Residual Variances I S Intercepts I S

25 Output Excerpts LSAY Growth Model With Free Time Scores And Covariates (Continued) R-Square Observed Variable Latent Variable I S R-Square R-Square Model Estimated Average And Individual Growth Curves With Covariates Model: y ti = η 0i + η 1i x t + ε ti, (23) η 0i = α 0 + γ 0 w i + ζ 0i, (24) η 1i = α 1 + γ 1 w i + ζ 1i, (25) Estimated growth factor means: Ê(η 0i ) = ˆ α 0+ ˆ0 γ w, (26) Ê(η 1i ) = ˆ α + γ. (27) 1 ˆ1 w Estimated outcome means: Ê(y ti ) = Ê(η 0i ) + Ê(η 1i ) x t. (28) Estimated outcomes for individual i : yˆ ti = ηˆ 0i + ηˆ 1i xt where ˆ0i η and ηˆ1 i are estimated factor scores. y ti can be used for prediction purposes. (29) 86 43

26 Model Estimated Means With Covariates Model estimated means are available using the TECH4 and RESIDUAL options of the OUTPUT command. Estimated Intercept Mean = Estimated Intercept + Estimated Slope (Mothed)* Sample Mean (Mothed) + Estimated Slope (Homeres)* Sample Mean (Homeres) * *3.11 = 52.9 Estimated Slope Mean = Estimated Intercept + Estimated Slope (Mothed)* Sample Mean (Mothed) + Estimated Slope (Homeres)* Sample Mean (Homeres) * *3.11 = Model Estimated Means With Covariates (Continued) Estimated Outcome Mean at Timepoint t = Estimated Intercept Mean + Estimated Slope Mean * (Time Score at Timepoint t) Estimated Outcome Mean at Timepoint 1 = * (0) = 52.9 Estimated Outcome Mean at Timepoint 2 = * (1.00) = Estimated Outcome Mean at Timepoint 3 = * (2.45) = Estimated Outcome Mean at Timepoint 4 = * (3.50) =

27 Estimated LSAY Curve 65 Outcome Grade 89 Centering 90 45

28 Centering Centering determines the interpretation of the intercept growth factor The centering point is the timepoint at which the time score is zero A model can be estimated for different centering points depending on which interpretation is of interest Models with different centering points give the same model fit because they are reparameterizations of the model Changing the centering point in a linear growth model with four timepoints Timepoints Centering at Time scores Timepoint Timepoint Timepoint Timepoint 4 91 Input Excerpts For LSAY Growth Model With Free Time Scores And Covariates Centered At Grade 10 MODEL: i s math7*-3 math8*-2 i s ON mothed homeres; Alternative language: MODEL: i BY s BY math7*-3 math8*-2 [i s]; i s ON mothed homeres; 92 46

29 Output Excerpts LSAY Growth Model With Free Time Scores And Covariates Centered At Grade 10 Tests of Model Fit CHI-SQUARE TEST OF MODEL FIT n = 935 Value Degrees of Freedom 7 P-Value.0265 RMSEA (ROOT MEAN SQUARE ERROR OF APPROXIMATION) Estimate Percent C.I Probability RMSEA <= Output Excerpts LSAY Growth Model With Free Time Scores And Covariates Centered At Grade 10 (Continued) Selected Estimates Estimates S.E. Est./S.E. Std StdYX I ON MOTHED HOMERES S ON MOTHED HOMERES

30 Further Readings On Introductory Growth Modeling Bijleveld, C. C. J. H., & van der Kamp, T. (1998). Longitudinal data analysis: Designs, models, and methods. Newbury Park: Sage. Bollen, K.A. & Curran, P.J. (2006). Latent curve models. A structural equation perspective. New York: Wiley. Muthén, B. & Khoo, S.T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, Special issue: latent growth curve analysis, 10, (#80) Muthén, B. & Muthén, L. (2000). The development of heavy drinking and alcohol-related problems from ages 18 to 37 in a U.S. national sample. Journal of Studies on Alcohol, 61, (#83) Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods. Second edition. Newbury Park, CA: Sage Publications. Singer, J.D. & Willett, J.B. (2003). Applied longitudinal data analysis. Modeling change and event occurrence. New York, NY: Oxford University Press. Snijders, T. & Bosker, R. (1999). Multilevel analysis. An introduction to basic and advanced multilevel modeling. Thousand Oakes, CA: Sage Publications. 95 Further Practical Issues 96 48

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