Lecture 6 Flashcards
What does path analysis look at?
- regular regression model
- observed variables only (no latent/unobserved variables)
When is correlation consistent with prediction? (According to Wright, 1921)
- temporal ordering of variables (cause before effect)
- covariation/correlation among variables
- other causes controlled for
What is a recursive model?
- unidirectional paths
- independent error/residuals
- can be tested with standard multiple regression
What are non-recursive models?
- bidirectional paths
- correlated error terms
- feedback loops
- need SEM programs (AMOS)
Why might you get different numbers in AMOS compared to normal regression? What numbers will differ?
if you don’t correlate the IVs/predictors
- regression weights will be the same
- SEs, standardised weights and squared multiple correlation (R2) will differ
Why is path analysis better than regression?
- gives more information (tells you which correlations b/w variables are significant, can remove to make more parsimonious)
- much more accurate
- really advantageous when there are latent variables predicting a further latent variable
What is a multi-step path analysis?
A > M > B
- A = predictor
- M = both predictor and predicted (intervening variable)
- B = predicted
What 2 ways can you do a multi-step analysis?
- you can do 2 regressions
- you can use AMOS (remember to correlate predictors)
What do you need to do to get accurate measures if you choose to do 2 regressions?
- R2 = combine R2 values together
- direct effects are normal
- indirect effects: need to multiply the two beta values together
Why do you need additional fit indices to X2?
- it is sensitive
- large sample: trivial diffs may be sig.
- small sample: may not be exactly X2 distributed > inaccurate probability levels
What are the comparative fit indices?
- NFI
- CFI
- RMSEA
What are the proportion of variance explained fit measures?
- GFI
- AGFI
What are the degree of parsimony fit indices?
- PGFI
- AIC
- CAIC
What are the residual based fit indices?
- RMR
- SRMR
Which fit indices do you usually report?
- if they agree, it is usually up to personal choice. Often report multiple.
COMMONLY:
CFI, RMSEA
maybe SRMR
AIC and CAIC for comparing models
What do you look at in the modification indices?
the MI values (expected decrease in X2)
What does it mean if AMOS says that some variances are negative?
it is an error message
NOT a good model
What do multivariate normality values means?
- less than 1 = negligible
- 1-10 = moderate non-normality
- 10+ = severe non-normality
What is mahalanobis distance?
measure of how close specific cases are to the centroid (the multivariate version of the mean)
When do you use bootstrapping? Why?
- when assumptions are not met
- when distribution is not normal
- bc: model can be incorrectly rejected as not fitting
- SEs can be smaller than they really are (this can make parameters seem sig. when they are not)
What is bootstrapping? What is diff about Bollen Stine compared to Naive?
- repeated samples of the data
- allow each observation to be taken more than once in any sample
- sampling with replacement
- calculate statistic (eg. mean) for each bootstrapped sample and make sampling distro/bootstrap distro.
- calc. SD for sampling distribution - this is the SE!!
Bollen-Stine: calculate ML for each BS sample to make bootstrap/sampling distro. Find 5% mark on distro.
When do you use Bollen-Stine bootstrapping? When would you use Naive bootstrapping?
- BS so assess overall fit
- Naive to get standard error
How is Bollen-Stine different to regular bootstrapping
- it transforms the parent sample into a sample with perfect fit
- the variance-covaraince matrix fits prefectly with the model BUT still has same distributional aspects of original parent sample
- thus would expect the bootstrapped sample to fit very well
- want parent to have better fit than 5% of the bootstrapped ones > suggests good fit
How do you interpret bootstrap iterations?
- BOLLEN: method 1, see if the parent fit better >5% of times
NAIVE:
- first SE = bootstrapped SE
- SE - SE = diff b/w bootstrap and usual
- “mean” = regression weight
- bootstrap CI: not include 0 = sig. parameter!
