Dear all,


In my experiment I look at a physiological response in two species in response to looking at their own or the other species.
I expect that a subject from species A shows an increased effect when looking at an individual of species A and subject B when looking at species B.

I made two models and wonder which one is better, from theoretical point of view and in terms of fit to the data.

In model1, I investigated a "subject_species" * "stimulus_species effect"


Information Criteriaa


-2 Log Likelihood


-24308,541


Akaike's Information Criterion (AIC)


-24272,541


Hurvich and Tsai's Criterion (AICC)


-24272,473


Bozdogan's Criterion (CAIC)


-24124,603


Schwarz's Bayesian Criterion (BIC)


-24142,603


The information criteria are displayed in smaller-is-better forms.


a. Dependent Variable: pupilsize_subject_dependent_variable.



In model2, I included a different variable "same_different" instead of the variable "stimulus_species". The "same_different" variable gives a "1" if species A looks at A and if B looks at B, and otherwise a "0". So basically, I modelled my hypotheses here (or at least, that's what I think I am doing).

Information Criteriaa


-2 Log Likelihood


-24382,297


Akaike's Information Criterion (AIC)


-24346,297


Hurvich and Tsai's Criterion (AICC)


-24346,229


Bozdogan's Criterion (CAIC)


-24198,359


Schwarz's Bayesian Criterion (BIC)


-24216,359


The information criteria are displayed in smaller-is-better forms.


a. Dependent Variable: pupilsize_subject_dependent_variable.



A more negative -2 Log Likelihood means a better fit, right? So from that view the second one is a bit better, although it probably doesn't matter much.

Any comments are very much appreciated!


best wishes,
Mariska

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