Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

DAPR3 HA > WK 7 > Flashcards

WK 7 Flashcards

1
Q

What does a path model allow us to do?

A

A path model allows us to test several linear models together as a set

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What are exogenous variables?

A

They are essentially independent variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What are endogenous variables?

A

They are dependent variables in at least one part of the model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What directions do the arrows go in exogenous variables?

A

Only have directed arrows going out (basically predictors)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What directions do the arrows go in endogenous variables?

A

They have directed arrows going in

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

In a diagram, what does a square represent?

A

It represents an observed/measured variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

In a diagram, what does a circle represent?

A

It represents an unobserved/latent variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

In a diagram, what does a two-headed arrow represent?

A

It represents covariance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

In a diagram, what does a single headed arrow represent?

A

It represents a regression path

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

In a diagram, when there is a single arrow, what is it showing?

A

The square that the arrow is pointing in to is the dependent variable/outcome, whereas the other side is the predictor

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What does every endogenous variable have?

A

They have a residual

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What do you need after you run a lavaan model?

A

you need to use a summary function

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

What is specification in path models?

A

It concerns which variables relate to which others, and in what ways

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

What are the standard rules in path models?

A
  1. all exogenous variables correlate
  2. for endogenous variables, we correlate the residuals, not the variables
  3. endogenous variable residuals do not correlate with exogenous variables
  4. all paths are recursive (i.e. we cannot have loops like A-> B, B-> A)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

What is model identification?

A

Identification concerns the number of knowns versus unknowns

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

In order to test our model, what do we need?

A

We need more knowns than unknowns in order to test our model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

What are the knowns?

A

The knowns are variances and covariances

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

What are path models based on?

A

path models are based on the correlation matrix between variables that you have measured

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

What are the unknowns?

A

The unknowns are the parameters we want to estimate

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

What are the degrees of freedom in model identification?

A

Degrees of freedom are the difference between knowns and unknowns

21
Q

What are the three levels of identification?

A

Under-identified, just identified, over-identified models

22
Q

What are the degrees of freedom for under-identified models?

A

They have <0 degrees of freedom

23
Q

What model won’t estimate?

A

Under-identified models

24
Q

What are the degrees of freedom for just identified models?

A

They have 0 degrees of freedom

25
Q

What is an example of just identified models?

A

standard linear models

26
Q

What are the degrees of freedom for over-identified models?

A

they have > 0 degrees of freedom

27
Q

What is model estimation?

A

It refers to finding the ‘best’ values for the unknown parameters

28
Q

What are the assumption of maximum likelihood estimation?

A
  • large sample size
  • multivariate normality
  • variables are on a continuous scale
29
Q

Why does no convergence happen?

A
  • the model is not identified
    -the model is mis-specified
    -the model is very complex, so more iterations are needed than the program default
30
Q

What does a statistically significant chi-squared suggest?

A

It suggests the model does not do a good job of reproducing the observed variance-covariance matrix

31
Q

What is the range of absolute fit?

A

ranges from 0 to 1

32
Q

What is deemed as perfect fit in absolute fit?

A

0=perfect fit

33
Q

What is considered good in the range for absolute fit?

A

values <.05 considered good

34
Q

What is a perfect parsimony-corrected indices?

A

0 = perfect fit

35
Q

What is considered good in the range for parsimony-corrected indices?

A

values <.05 considered good

36
Q

What does absolute fit measure?

A

It measures the discrepancy between the observed correlation matrix and model-implied correlation matrix

37
Q

What does parsimony-corrected indices do?

A

Adds a penalty for having more degrees of freedom

38
Q

What does incremental fit indices do?

A

It compares the model to a more restricted baseline model

39
Q

What is the range of a comparative fit infex (CFI)?

A

Ranges between 0 and 1

40
Q

What value is the perfect fit, and what values are considered good in comparative fit indices?

A

1= perfect fit
values > 0.95 considered good

41
Q

What does the Tucker-Lewis index (TLI) include?

A

Includes a parsimony penalty

42
Q

What values are considered good in tucker-lewis index?

A

Values >0.95 are considered good

43
Q

What is modification indices?

A

Modification indices provides the improvement in fit

44
Q

What do expected parameter changes estimate?

A

Estimate the value of the parameter were it to be included

45
Q

How do you extract modification indices and expected parameter chnges?

A

using summary(model, mod.indices=T)

46
Q

When is chi-squared statistic significant?

A

values < 0.05

47
Q

What is absolute fit?

A

standardised root mean square residual (SRMR)

48
Q

What is parsimony-corrected?

A

root mean square error of approximation (RMSEA)

DAPR3 HA (5 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions