Stack Overflow Asked by Lan on August 6, 2020

I am doing a statistical analysis by using Type-III Anova on an ordinal regression model with "clmm" function. My initial code is shown below:

```
require (ordinal)
require (RVAideMemoire)
require (car)
Risk <- factor (DWQ$Risk_Perception, ordered = T)
RP <- clmm(Risk ~ Location + Consequence + Distraction_Type + Location * Consequence + Location * Distraction_Type + (1|Subject), data = DWQ, link = "probit")
summary(RP)
Anova.clmm (RP, type = 3)
```

In this regression model, "Risk" is ordinal data from 1 to 5. The three independent variables, Location(5 levels), Distraction_type(2 levels), Consequences(3 levels), are written in non-numeric format. The "clmm" function does support for type-III Anova analysis. However, in this case, **the algorithm forces me to use Type II Anova, and the result is also strange** (shown below):

Anova.clmm (RP, type = 3)

Analysis of Deviance Table (Type II tests)

Response: Risk

```
LR Chisq Df Pr(>Chisq)
Location 0.00 4 1.0000000
Consequence 0.00 2 0.9999998
Distraction_Type 0.00 1 0.9995538
Location:Consequence 469.80 8 < 2.2e-16 ***
Location:Distraction_Type 18.56 4 0.0009596 ***
```

The value of the Chi-sq test is zero, and p values are not correct. If I change the Anova test to type-II, it seems that everything goes right (shown below):

Anova.clmm (RP, type = 2)

Analysis of Deviance Table (Type II tests)

Response: Risk

```
LR Chisq Df Pr(>Chisq)
Location 3026.47 4 < 2.2e-16 ***
Consequence 38.20 2 5.063e-09 ***
Distraction_Type 1494.63 1 < 2.2e-16 ***
Location:Consequence 469.80 8 < 2.2e-16 ***
Location:Distraction_Type 18.56 4 0.0009596 ***
```

What might be causing this problem?

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