Homework:
1. Provide the value for (using the correct acronym and three decimal places) and
interpret (in no more than one sentence) the comparative fit index, the Tucker-Lewis
index, the root mean sq
C. Standardized factor variance
A way to show that these two models are nested and directly output the Dc2 is to
parameterize the model differently
Recall the r, .84, between Nonverbal Reasoning and S
Model 1 fits just as well as the more complex. For this check out the CMIN number.
E. Rules of thumb for post hoc modifications
1. Examine larger values first
The size of MIs, like c2, are sample dep
Pattern Coefficient
Pattern Coefficient (pxm)
P = rows
M = columns
If there is not a path between the variable and the factor the direct effect is fixed to zero
Text Output estimates Scalar Regression
Results may be relative, not absolute
3. Model Generation (MG) exploratory
a. Goal:
a.i.
search for the best fitting, substantively meaningful model, with the same
data, after initial model has been r
Loading Notes
More common will be to set a loading to one, while at other times you may have to set a
variance to 1.
Common factors to the left, non-common to the right
2. 4 latent constructs: Vc (com
Latent Means Models
This lecture is all about Latent growth modeling curves.
Means havent been in your model. You can find means in your models with what you
have.
You are interested in latent means,
Hierarchical Models
G = general intelligence
In this model, verbal, non reasoning, spatial, memory are no longer exogenous variables.
You need to add residuals to these variables now. UF1
In the model
Confirmatory Factor Analysis
Some of these issues are not unique to CFA, just wanted to get solid footing in path
analysis before introduced to them.
Not really a data reduction technique.
Low correla
Variance of Tests
1. Test 1: participants read a passage on one page and then are asked to select 1
of 4 images that best illustrates the passage
Test 1: RC + ability to translate something read into