Hello everyone,

I would really appreciate some advice on how to interpret my SPSS-Output. I have tried to figure it out myself, but haven't been able to get an answer.

I have 2 binary independent variables and 1 dependent variable with 3 categorical outcomes. So a 2x2 Design.

In order to run multinomial logistic regression analyses, I put the two Factors into the factor-box and specified the model to check for 2 main effects and 1 interaction effect. However, after running SPSS I noticed that only the interaction effect was displayed. The 2 main effects however did not appear (i.e., the Chi2 is ,000 and no significance level is shown for each of the main effect). There is also a note that says:

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

a) This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.

HOWEVER, SPSS gave me the main effects and the interaction effect, when I defined the factors as covariates (i.e. I put the factors into the covariate-box). However, the factors are nominal, so shouldn't be defined as covariates.

Thank you very much in advance! :tup:

I would really appreciate some advice on how to interpret my SPSS-Output. I have tried to figure it out myself, but haven't been able to get an answer.

I have 2 binary independent variables and 1 dependent variable with 3 categorical outcomes. So a 2x2 Design.

In order to run multinomial logistic regression analyses, I put the two Factors into the factor-box and specified the model to check for 2 main effects and 1 interaction effect. However, after running SPSS I noticed that only the interaction effect was displayed. The 2 main effects however did not appear (i.e., the Chi2 is ,000 and no significance level is shown for each of the main effect). There is also a note that says:

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

a) This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.

HOWEVER, SPSS gave me the main effects and the interaction effect, when I defined the factors as covariates (i.e. I put the factors into the covariate-box). However, the factors are nominal, so shouldn't be defined as covariates.

Thank you very much in advance! :tup:

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