Confused: Why is lme4 changing techniques from Wald F tests to Wald Chisquare? I've constructed some LMEMs that use dichotomous variables and their interactions as regressors, and I've become confused by the output. When I only assess a single interaction, using the code below (which is the approach provided by my statistical training), lme4 performs a Type III Wald F test:
value3 <- lmer(neurosynth_value ~ contrast*Teen_vs_Adult + (1 | participant_id),dCon)

Anova(value3, type= 3, test = 'F') 

The output looks like this, which is exactly what I want:
Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)

Response: neurosynth_value
                            F Df Df.res
(Intercept)            5.9460  1 328.06
contrast               5.9198  1 511.34
Teen_vs_Adult          3.4148  1 329.88
contrast:Teen_vs_Adult 1.5901  1 509.75
                        Pr(>F)  
(Intercept)            0.01528 *
contrast               0.01531 *
Teen_vs_Adult          0.06551 .

BUT when I add an additional dichotomous regressor and its interactions, the Anova function changes methods to perform a Type III Wald Chi-Square? That code and results are below.
Code:
valuecon1 <- lmer(neurosynth_value ~ contrast*valence*Teen_vs_Adult + (1 | participant_id),dConValue)

Anova(conflictcon1, type = 3, Test = 'F')

Output:
Analysis of Deviance Table (Type III Wald chisquare tests)

Response: neurosynth_conflict
                                 Chisq Df Pr(>Chisq)    
(Intercept)                    95.4341  1  < 2.2e-16 ***
contrast                       16.9597  1  3.818e-05 ***
valence                         4.0655  1   0.043768 *  
Teen_vs_Adult                  32.8155  1  1.013e-08 ***
contrast:valence                8.0806  1   0.004474 ** 
contrast:Teen_vs_Adult         10.6639  1   0.001092 ** 
valence:Teen_vs_Adult           2.4147  1   0.120200    
contrast:valence:Teen_vs_Adult  6.2718  1   0.012268 *  

Does anyone know why this is?

It's not
Anova(conflictcon1, type = 3, Test = 'F')

but rather
Anova(conflictcon1, type = 3, test.statistic = 'F')

