Download cjt765 class 12

CJT 765:
Structural Equation Modeling
Class 12:
Wrap Up: Latent Growth Models,
Pitfalls, Critique and
Future Directions for SEM
Outline of Class
Issues related to Final Project
Latent Growth Models using Mean
Structures
Pitfalls
Critique
Future Directions
Concluding Thoughts
Issues related to Final Project
 Final draft due Monday Apr. 18, 9 a.m., but never has to be
turned in understanding you then won’t get feedback for
presentation.
 You do:
 have to have measurement and structural components in
model
 need to talk about theoretical background, measures, and
implications as well as model testing
 have to test measurement components first, structural and
measurement components combined second
 have to consider improving your initial model, considering
removing paths, removing variables, or adding correlations
or paths
 You don’t:
 have to test more than 1 model
 have to test the most complex model possible
 have to have an adequate model
Latent Growth Models
Latent Growth Models in
SEM are often structural
regression models with
mean structures
Mean Structures
Means are estimated by regression of
variables on a constant
Parameters of a mean structure include
means of exogenous variables and
intercepts of endogenous variables.
Predicted means of endogenous variables
can be compared to observed means.
Principles of Mean Structures in SEM
 When a variable is regressed on a predictor and
a constant, the unstandardized coefficient for the
constant is the intercept.
 When a predictor is regressed on a constant, the
undstandardized coefficient is the mean of the
predictor.
 The mean of an endogenous variable is a
function of three parameters: the intercept, the
unstandardized path coefficient, and the mean of
the exogenous variable.
Additional Mean Structure Principles
 The predicted mean for an observed variable is
the total effect of the constant on that variable.
 For exogenous variables, the unstandardized
path coefficient for the direct effect of the
constant is a mean; for endogenous variables,
the direct effect of the constant is an intercept
but the total effect is a mean.
Requirements for LGM
within SEM
 continuous dependent variable measured on at
least three different occasions
 scores that have the same units across time,
can be said to measure the same construct at
each assessment, and are not standardized
 data that are time structured, meaning that
cases are all tested at the same intervals (not
need be equal intervals)
Steps for Latent Growth Models
within SEM
Evaluate a change model that involves just
the repeated measures variables
Add predictors to the model by regressing
the latent growth factors on the predictors
Pitfalls--Specification
Specifying the model after data collection
Insufficient number of indicators. Kenny:
“2 might be fine, 3 is better, 4 is best, more
is gravy”
Carefully consider directionality
Forget about parsimony
Add disturbance or measurement errors
without substantive justification
Pitfalls--Data
Forgetting to look at missing data patterns
Forgetting to look at distributions, outliers,
or non-linearity of relationships
Lack of independence among
observations due to clustering of
individuals
Pitfalls—Analysis/Respecification
 Using statistical results only and not theory to
respecify a model
 Failure to consider constraint interactions and
Heywood cases (illogical values for parameters)
 Use of correlation matrix rather than covariance
matrix
 Failure to test measurement model first
 Failure to consider sample size vs. model
complexity
Pitfalls--Interpretation
 Suggesting that “good fit” proves the model
 Not understanding the difference between good
fit and high R2
 Using standardized estimates in comparing
multiple-group results
 Failure to consider equivalent or (nonequivalent)
alternative models
 Naming fallacy
 Suggesting results prove causality
Critique
The multiple/alternative models problem
The belief that the “stronger” method and
path diagram proves causality
Use of SEM for model modification rather
than for model testing. Instead:
Models should be modified before SEM is
conducted or
Sample sizes should be large enough to modify
the model with half of the sample and then
cross-validate the new model with the other half
Future Directions
Assessment of interactions
Multiple-level models
Curvilinear effects
Dichotomous and ordinal variables
Final Thoughts
 SEM can be useful, especially to:
separate measurement error from structural
relationships
assess models with multiple outcomes
assess moderating effects via multiple-sample analyses
consider bidirectional relationships
 But be careful. Sample size concerns, lots of
model modification, concluding too much, and
not considering alternative models are especially
important pitfalls.