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# Growth Curve Model Using SEM - PowerPoint PPT Presentation

Growth Curve Model Using SEM. David A. Kenny. Thanks due to Betsy McCoach. Linear Growth Curve Models. We have at least three time points for each individual. We fit a straight line for each person: The parameters from these lines describe the person. Nonlinear growth models are possible.

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Growth Curve Model Using SEM

David A. Kenny

Linear Growth Curve Models

• We have at least three time points for each individual.

• We fit a straight line for each person:

• The parameters from these lines describe the person.

• Nonlinear growth models are possible.

• Slope: the rate of change

• Some people are changing more than others and so have larger slopes.

• Some people are improving or growing (positive slopes).

• Some are declining (negative slopes).

• Some are not changing (zero slopes).

• Intercept: where the person starts

• Error: How far the score is from the line.

• For both the slope and intercept there is a mean and a variance.

• Mean

• Intercept: Where does the average person start?

• Slope: What is the average rate of change?

• Variance

• Intercept: How much do individuals differ in where they start?

• Slope: How much do individuals differ in their rates of change: “Different slopes for different folks.”

• measures taken over time

• chronological time: 2006, 2007, 2008

• personal time: 5 years old, 6, and 7

• missing data not problematic

• person fails to show up at age 6

• unequal spacing of observations not problematic

• measures at 2000, 2001, 2002, and 2006

• Types

• Raw data

• Covariance matrix plus means

Means become knowns: T(T + 3)/2

Should not use CFI and TLI (unless the independence model is recomputed; zero correlations, free variances, means equal)

• Program reproduces variances, covariances (correlations), and means.

• No correlations, free variances, and equal means.

• df of T(T + 1)/2 – 1

• Latent intercept factor and latent slope factor

• Slope and intercept factors are correlated.

• Error variances are estimated with a zero intercept.

• Intercept factor

• free mean and variance

• free mean and variance

• Standard specification (given equal spacing)

• and so on

• A one unit difference defines the unit of time. So if days are measured, we could have time be in days (0 for day 1 and 1 for day 2), weeks (1/7 for day 2), months (1/30) or years (1/365).

• At time zero, the intercept is defined.

• Rescaling of time:

• standard approach

• 0 loading at the last wave ─ centered at final status

• useful in intervention studies

• 0 loading in the middle wave ─ centered in the middle of data collection

• intercept like the mean of observations

• Same

• model fit (c2 or RMSEA)

• slope mean and variance

• error variances

• Different

• mean and variance for the intercept

• slope-intercept covariance

some intercept variance, and slope and intercept being positively correlated

no intercept variance

intercept variance, with slope and intercept being negatively correlated

Identification positively correlated

• Need at least three waves (T = 3)

• Need more waves for more complicated models

• Knowns = number of variances, covariances, and means or T(T + 3)/2

• So for 4 times there are 4 variances, 6 covariances, and 4 means = 14

• Unknowns

• 2 variances, one for slope and one for intercept

• 2 means, one for the slope and one for the intercept

• T error variances

• 1 slope-intercept covariance

Model df positively correlated

• Known minus unknowns

• General formula: T(T + 3)/2 – T – 5

• Specific applications

• If T = 3, df = 9 – 8 = 1

• If T = 4, df = 14 – 9 = 5

• If T = 5, df = 20 – 10 = 10

Three-wave Model positively correlated

• Has one df.

• The over-identifying restriction is:

M1 + M3 – 2M2 = 0

(where “M” is mean)

i.e., the means have a linear relationship with respect to time.

Intercept Factor positively correlated

Slope Factor positively correlated

Alternative Options for Error Variances positively correlated

• Force error variances to be equal across time.

• Non-independent errors

• errors of adjacent waves correlated

• autoregressive errors (err1  err2  err3)

Trimming Growth Curve Models positively correlated

• Almost never trim

• Slope-intercept covariance

• Intercept variance

• Never have the intercept “cause” the slope factor or vice versa.

• Slope variance: OK to trim, i.e., set to zero.

• If trimmed set slope-intercept covariance to zero.

• Do not interpret standardized estimates except the slope-intercept correlation.

Relationship to Multilevel Modeling (MLM) positively correlated

• Equivalent if ML option is chosen

• Measures of absolute fit

• Easier to respecify; more options for respecification

• More flexibility in the error covariance structure

• Allows latent covariates

• Allows missing data in covariates