A Effect size B Fratio 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 ID: 445784
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Slide1
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 testSlide2
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 testSlide3
Which of the following is not true
? Calculating regions of significance…
provides a test of the significance of conditional main effects across levels or regions of a moderator.
requires
careful consideration of centering of predictor variables
can be especially useful when no particular values of predictors are meaningful
is useful for decomposing a variety of higher-order interactions
helps to avoid the problem of wrongly interpreting a “non-significant” main effect.Slide4
Which of the following is not true? Calculating regions of significance…
provides a test of the significance of conditional main effects across levels or regions of a moderator.
requires
careful consideration of centering of predictor variables
can be especially useful when no particular values of predictors are meaningful
is useful for decomposing a variety of higher-order interactions
helps to avoid the problem of wrongly interpreting a “non-significant” main effect.Slide5
CenteringSlide6
In the continuous predictor case, which model effects will change as a result of choosing a different centering point?
Intercept
Main effects of predictors
Interaction effects of predictors
Model predicted scores
Definitely two, but possibly three of the above.Slide7
In the continuous predictor case, which model effects will change as a result of choosing a different centering point?
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.Slide8
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. FalseSlide9
Centering has no effect at all on linear regression coefficients (except for the intercept) unless at least one interaction term is included
.
A.
True
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 x-axis
.”
Kris Preacher:
http
://
quantpsy.org/interact/interactions.htm
Slide10
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. FalseSlide11
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
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 model-predicted outcomes would not change.Slide12Slide13Slide14