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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

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2
Q

What are exogenous variables?

A

They are essentially independent variables

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3
Q

What are endogenous variables?

A

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

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4
Q

What directions do the arrows go in exogenous variables?

A

Only have directed arrows going out (basically predictors)

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5
Q

What directions do the arrows go in endogenous variables?

A

They have directed arrows going in

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6
Q

In a diagram, what does a square represent?

A

It represents an observed/measured variable

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7
Q

In a diagram, what does a circle represent?

A

It represents an unobserved/latent variable

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8
Q

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

A

It represents covariance

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9
Q

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

A

It represents a regression path

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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

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11
Q

What does every endogenous variable have?

A

They have a residual

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12
Q

What do you need after you run a lavaan model?

A

you need to use a summary function

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13
Q

What is specification in path models?

A

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

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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)
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15
Q

What is model identification?

A

Identification concerns the number of knowns versus unknowns

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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

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17
Q

What are the knowns?

A

The knowns are variances and covariances

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18
Q

What are path models based on?

A

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

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19
Q

What are the unknowns?

A

The unknowns are the parameters we want to estimate

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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)

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