Normality and tests

obh

Active Member
#22
Unlike the linear regression, the logistic regression is a bit confusing to interpret... the result is the probability to get a specific value

Test for significance is not everything, it is important to know how does it influence.

In your case you may get a result like: "The fact that a person knows a science (KS=yes) will increase the odds of "pos emb"=1 in comparison to "pos emb"=0 by 23%. You may play with http://www.statskingdom.com/430logistic_regression.html for interpretation in parallel to your regular stat software.
 
#23
Test for significance is not everything, it is important to know how does it influence.
When you say that significance is not everything, what does that mean for my results? Using your example, if the results show that odds increase by 23%, but p > 0.05, does that mean that knowing a scientist does not have an impact on positive embodiment?
 

noetsi

Fortran must die
#24
What test would I use to compare a binary predictor to a binary DV, though? It wouldn't still be logistic regression, would it?
It does not matter what the predictor is. Only the DV. There are no predictor distribution assumptions formally in regression. If the DV is categorical and has two levels than you use logistic regression or probit (in practice these are different ways to do the same thing, they use a different link function to do it). If your DV has 3 levels (or 4 or 5) you would use multinomial logistic regression if they are nominal or ordinal logistic regression if they can be logically ordered.
 

noetsi

Fortran must die
#25
Unlike the linear regression, the logistic regression is a bit confusing to interpret... the result is the probability to get a specific value

Test for significance is not everything, it is important to know how does it influence.

In your case you may get a result like: "The fact that a person knows a science (KS=yes) will increase the odds of "pos emb"=1 in comparison to "pos emb"=0 by 23%. You may play with http://www.statskingdom.com/430logistic_regression.html for interpretation in parallel to your regular stat software.
Odds ratios or proportional risk are probably the best way to interpret them. You use something like negative 2 log likelihood to test the model and Wald tests to test individual parameters.
 

obh

Active Member
#26
When you say that significance is not everything, what does that mean for my results? Using your example, if the results show that odds increase by 23%, but p > 0.05, does that mean that knowing a scientist does not have an impact on positive embodiment?
I meant that you need to look at both.

If we ignore for a moment the multiple tests problem, and under the assumption that you took the proper sample size:
P value=0.23 says only that there is a probability of 0.23 that there is no effect.
or there may be a meaningless effect, per your definition, that is smaller than the required effect you used to calculate the sample size.

But if P value=0.00001, you know there is an effect, but it doesn't say anything about the effect size.

For example, you may find that medication improves the disease healing process with P value=0.00001, but the effect is very small, like healing time will be improved from 90 days to 89.8 days. you probably won't use this medication ...
 

noetsi

Fortran must die
#30
_cons is significant at the .05 level. It is the only variable that is. 1=knows has a p value of .055 so it is not significant at the .05 level (one way to know this is the CI contains 1 - if it does the variable is not statistically significant in logistic regression).

Is _cons interval or a categorical predictor (and how is it coded. For example 1/0 if it has two levels).

I did not see an intercept. Did you suppress it? If you did it changes the interpretation (which is why I don't suppress it). :p
 
#31
_cons is significant at the .05 level. It is the only variable that is. 1=knows has a p value of .055 so it is not significant at the .05 level (one way to know this is the CI contains 1 - if it does the variable is not statistically significant in logistic regression).

Is _cons interval or a categorical predictor (and how is it coded. For example 1/0 if it has two levels).

I did not see an intercept. Did you suppress it? If you did it changes the interpretation (which is why I don't suppress it). :p
_cons is the constant/intercept
 

obh

Active Member
#32