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ADDA > Lecture 5 > Flashcards

Lecture 5 Flashcards

1
Q

Why is SEM good?

A
  • allows for complicated analysis that would usually take many steps
  • allow for analysis of more than one relationship at one time
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2
Q

What shapes represent what things in SEM?

A
  • rectangle: observed/manifest
  • oval/ellipse: latent factor
  • circle: error/residual (also unobserved)
  • curved, double-arrow: correlation
  • straight, single-arrow: regression
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3
Q

Why is CFA superior to EFA?

A
  • can restrict factor loadings to take certain values (commonly 0)
  • not all items have to load on all factors (lots of 0s implied)
  • CFA has a test of fit; test if data fits the model to see if you have support for your hypothesis
  • CFA: you assign the items to the factor, (in EFA, computer does it) test a theory

in EFA, can only restrict no. of factors and loadings to be uncorrelated (w orthogonal)

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4
Q

What’s the deal with sample size and CFA? What situations do you need a larger sample size?

A
  • 200+
  • large scale technique
  • many fit indices are ASYMPTOTIC (become more like distro. as sample size increases)
  • larger is better (400+)
  • > 10 (20:1 is good) subjects for parameters
  • no hard, fast rules
  • more parameters = need larger sample
  • missing data = need larger sample
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5
Q

What are the distributional assumptions of CFA? How can you get around this?

A
  • assume multivariate normality (can check this in AMOS)
  • ML is robust to mod. violations
  • there are some other fully robust methods that you can use
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6
Q

What is the assumption of levels of measurement? Why?

A
  • assumes continuous observed variables
  • bc. working with variances and covariances, NOT raw data
  • some programs do allow for ordered categorical data
  • latent variables always assumed to be continuous
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7
Q

Explain identification

A
  • CFA seeks parameter estimates that can best reproduce the variance-covariance matrix (MODELs this)
  • underdetermined: not enough to estimate, need a new model
  • just determined: exact no. of equations, no df/freedom, fits exactly
  • overdetermined: what you want, more than enough equations > want more unique variance-covariance terms than parameters to estimate
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8
Q

How do you get identified CFA models? Why does this work?

A
  • at least 3 observed variables loading on each factor
  • fix one loading on each factor to a non-zero value (usually 1)
  • this sets the scale of the factor to the scale of the variable > this establishes that the factor is measured on the same scale as the variable
  • also set error covariance to 1 > determines scale for these
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9
Q

How do you assess model fit in CFA?

A
  • chi-square (but sensitive)
  • absolute: SRMR
  • comparative: CFI
  • parsimony: RMSEA
    ^^^ cite all!
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10
Q

What do you need to interpret in the CFA AMOS output?

A
  • unstandardised and standardised parameter estimates
  • modification indices
  • assumptions of normality (don’t want c.r. above 2)
  • model fit
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11
Q

What are observed variables sometimes called?

A

manifest variables

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12
Q

Why are error terms in circles?

A

because they are not directly observed

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13
Q

What is the measurement vs. structural model in CFA?

A
  • measurement: the factor analysis, shows how the latent variables are created
  • structural: the prediction of the constructs, regression
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14
Q

What type of model is CFA?

A

measurement

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15
Q

What is an identified model? What do you need for this?

A
  • a model that be estimated unambigiously

- need to place constraints on the model (NOT data) for this to occur

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16
Q

What does it mean by CFA seeks parameters that best reproduce the variance-covariance matrix?

A
  • parameters: factor loadings, factor correlations, unique variances (residuals)
  • creates it’s own variance-covariance matrix based on these and you can then test fit
17
Q

How do you know how many variances and covariances there are?

A

n(n+1)/2

  • can draw up a table of the variables vs. the variables
  • eg. 2 x 2 = 2 unique variances (one for each); 1 covariance repeated twice in table
18
Q

Why is chi-square bad as a model fit measure?

A
  • large sample: say it doesn’t fit
  • small sample: say it does fit (insufficient power to reject)

^^ strange

19
Q

How do you interpret CFA parameter estimates?

A
  • a one unit increase in the latent variable results in a one unit increase/decrease in the item, holding constant scores on the other latent factor(s)
  • standardised: standard deviation units
20
Q

What are distinct sample moments?

A
  • sample means, variances and covariances

- BUT amos ignores variances and covariances, therefore it’s only the variances and covarirances

21
Q

What are the 2 types of SEM we learnt about?

A
  • CFA

- path analysis

22
Q

When do you not have an error/residual term?

A
  • when there is no arrow pointing TO that variable in the model (i.e. nothing it predicting it)
23
Q

What is the key difference between EFA and CFA in terms of factor loadings?

A
  • EFA assumes all load on all latent variables (lots more lines in model!). Can suppress them, but they are still there
  • CFA restricts many to be zero, not all load on all
24
Q

What do absolute, comparative and parsimony measures of fit do?

A
  • absolute: proportions of covariances in sample data matrix explained by model
  • comparative: relative improvement in fit compared to baseline (baseline = no common factors, all variables are independent)
  • parsimony: model-sample discrepancy adjusted for sample size and number of parameters; correction to “punish” more complex models
25
Q

What does AMOS stand for?

A

Analysis of Moment Structures

26
Q

How do you tell if a model is identified by looking at the AMOS output?

A
  • df positive

- number of parameters to estiamte to be less than distinct sample moments

27
Q

What does the MI value actually mean?

A
  • conservative estimate of the decrease in X2

> the improvement in fit

28
Q

If there are at least 2 factors, what is required for an identified model with only 2 variables loading per factor?

A
  • factor correlated with another factor
  • 1 variable loading is fixed to non-zero value

^^ for more complex models, these 2 rules may not apply

ADDA (15 decks)

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