In general, for any fixed effect the ratio of its
estimate divided by its standard error is
knows as
• A. Effect size
• B. F-ratio
• C. Conditional marginal mean
• D. Log Likelihood
• E. Wald test
In general, for any fixed effect the ratio of its
estimate divided by its standard error is
knows as
• A. Effect size
• B. F-ratio
• C. Conditional marginal mean
• D. Log Likelihood
• E. Wald test
Which of the following is not true? Calculating
regions of significance…
A. provides a test of the significance of conditional main
effects across levels or regions of a moderator.
B. requires careful consideration of centering of predictor
variables
C. can be especially useful when no particular values of
predictors are meaningful
D. is useful for decomposing a variety of higher-order
interactions
E. helps to avoid the problem of wrongly interpreting a “nonsignificant” main effect.
Which of the following is not true? Calculating
regions of significance…
A. provides a test of the significance of conditional main
effects across levels or regions of a moderator.
B. requires careful consideration of centering of predictor
variables
C. can be especially useful when no particular values of
predictors are meaningful
D. is useful for decomposing a variety of higher-order
interactions
E. helps to avoid the problem of wrongly interpreting a “nonsignificant” main effect.
Centering
In the continuous predictor case, which model
effects will change as a result of choosing a
different centering point?
A.
B.
C.
D.
E.
Intercept
Main effects of predictors
Interaction effects of predictors
Model predicted scores
Definitely two, but possibly three of the above.
In the continuous predictor case, which model
effects will change as a result of choosing a
different centering point?
A.
B.
C.
D.
E.
Intercept
Main effects of predictors
Interaction effects of predictors
Model predicted scores
Definitely two, but possibly three of the above.
The intercept, conditional main effects, and all but the highest-order
interaction (e.g., 3-way; age X sex X grip) will change.
Centering has no effect at all on linear
regression coefficients (except for the
intercept) unless at least one interaction term
is included.
A. True
B. False
Centering has no effect at all on linear
regression coefficients (except for the
intercept) unless at least one interaction term
is included.
A. True
B. False
“Regardless of the complexity of the regression equation, centering has no
effect at all on the coefficients of the highest-order terms, but may drastically
change those of the lower-order terms in the equation. The algebra is given in
Aiken and West (1991), but centering unstandardized IVs usually does not affect
anything of interest. Simple slopes will be the same in centered as in
uncentered equations, their standard errors and t-tests will be the same, and
interaction plots will look exactly the same, but with different values on the xaxis.”
Kris Preacher: http://quantpsy.org/interact/interactions.htm
Centering predictors (i.e., changing the zero
point) permits the evaluation of the main
effect of its interacting predictors at specific
points of interest.
A. True
B. False
Centering predictors (i.e., changing the zero point)
permits the evaluation of the main effect of its
interacting predictors at specific points of interest.
A. True
B. False
For example, to test the sex difference in cognition for 80 year
olds, center age at 80 years (actual age – 80). This would
result in the interaction term of sex X age to be 0 in the
estimated model, permitting a direct test of Male/Female
differences at age 80 (where age=0). However, the modelpredicted outcomes would not change.