> fit4 <- glm(categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_FODs - 1, + family = binomial(link = 'logit'), data = indvar_FODs) > summary(fit4) Call: glm(formula = categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_FODs - 1, family = binomial(link = "logit"), data = indvar_FODs) Deviance Residuals: Min 1Q Median 3Q Max -1.315 -1.159 -1.111 1.191 1.268 Coefficients: Estimate Std. Error z value Pr(>|z|) Condition_FODsA:Gender_FODsFemale 0.06351 0.10292 0.617 0.537 Condition_FODsB:Gender_FODsFemale -0.04256 0.10317 -0.413 0.680 Condition_FODsC:Gender_FODsFemale -0.03175 0.10288 -0.309 0.758 Condition_FODsD:Gender_FODsFemale -0.10648 0.10329 -1.031 0.303 Condition_FODsA:Gender_FODsMale 0.07733 0.09837 0.786 0.432 Condition_FODsB:Gender_FODsMale -0.15937 0.09849 -1.618 0.106 Condition_FODsC:Gender_FODsMale -0.11496 0.09798 -1.173 0.241 Condition_FODsD:Gender_FODsMale 0.03865 0.09831 0.393 0.694 Condition_FODsA:Gender_FODsNon-Binary / Genderqueer 0.31845 0.32855 0.969 0.332 Condition_FODsB:Gender_FODsNon-Binary / Genderqueer 0.21131 0.32625 0.648 0.517 Condition_FODsC:Gender_FODsNon-Binary / Genderqueer 0.21131 0.32625 0.648 0.517 Condition_FODsD:Gender_FODsNon-Binary / Genderqueer -0.21131 0.32625 -0.648 0.517 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4603.9 on 3321 degrees of freedom Residual deviance: 4595.2 on 3309 degrees of freedom (199 observations deleted due to missingness) AIC: 4619.2 Number of Fisher Scoring iterations: 3 > m2 <- clm(categorical_FOD_FODs ~ 0 + Condition_FODs:Handedness_FODs - 1, + random = ~1|subject, data = indvar_FODs) > summary(m2) formula: categorical_FOD_FODs ~ 0 + Condition_FODs:Handedness_FODs - 1 data: indvar_FODs link threshold nobs logLik AIC niter max.grad cond.H logit flexible 3321 -2912.62 5843.25 6(0) 9.51e-13 1.3e+02 Coefficients: (1 not defined because of singularities) Estimate Std. Error z value Pr(>|z|) Condition_FODsA:Handedness_FODsLeft-handed 0.19984 0.32178 0.621 0.5346 Condition_FODsB:Handedness_FODsLeft-handed -0.03557 0.32239 -0.110 0.9122 Condition_FODsC:Handedness_FODsLeft-handed -0.22559 0.32812 -0.688 0.4918 Condition_FODsD:Handedness_FODsLeft-handed 0.44106 0.33746 1.307 0.1912 Condition_FODsA:Handedness_FODsRight-handed 0.14802 0.09944 1.489 0.1366 Condition_FODsB:Handedness_FODsRight-handed -0.18684 0.09828 -1.901 0.0573 . Condition_FODsC:Handedness_FODsRight-handed -0.13824 0.09810 -1.409 0.1588 Condition_FODsD:Handedness_FODsRight-handed NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Threshold coefficients: Estimate Std. Error z value ascending|descending -0.01590 0.07086 -0.224 descending|identity 2.68193 0.09465 28.336 (199 observations deleted due to missingness) what is the difference between these two tests above?
The difference between these two tests is that the first is a fixed effect test, and the second is a mixed effects test (random effect). The first test is a multinomial logistic regression. We are testing whether there is a significant interaction between condition and gender on a categorical DV that is a factor of FOD. The results show that the interaction between condition and gender is not significant. The second test is a mixed effects multinomial regression. We are testing whether there is a significant interaction between condition and handedness on a categorical DV that is a factor of FOD. The results show that the interaction between condition and handedness is significant. Since both are multinomial logistic regressions, we have to use a multinomial test to determine whether there is a significant main effect of condition on FOD. To do this we tested the effect of condition on FOD using a multinomial logistic regression. The results of this test showed that there was not a significant main effect of condition on FOD.
