#### Background

I am running a mixed linear model with four fixed predictors and (most of) their interactions. I am quite new to these models and I am trying to understand how I should interpret my results and whether I am using the correct approach to generate them. 

#### My Model

My model contains two **factor** variables: (1) Group_Variable with *three* levels: 'Group_1_H', 'Group_2_L','Group_3_T'; (2) Variable_C with *two* levels: 'level1_O','Level2_S'.

And it contains two **continuous** variables: Variable_A and Variable_B. The dependent variable is continuous.

My prediction is that there will be an interaction between **Group_Variable** and **Variable_C**, whereby Group_1_H will show have a significantly different score on the outcome variable based on whether they are in condition 'level1_O' compared to 'level2_S' relative to the other two groups. 

Here is the model specification:

```
model <- my_outcome ~  lmerTest::lmer(Group_Variable * Variable_A * Variable_C + 
                                      Group_Variable * Variable_B * Variable_C + 
                                      (1 | ID_Variable), data = mydata)
```

Here is my output when I use the summary() function:

[![Summary][1]][1]


And here is the output when I use the Anova function from the car package:

[![enter image description here][2]][2]


And the post hoc pair-wise contrasts:

|Group_Variable_pairwise |Variable_C_pairwise |   estimate|        SE|       df|   t.ratio|   p.value|
|:-----------------------|:-------------------|----------:|---------:|--------:|---------:|---------:|
|Group_3_T - Group_1_H   |level1_O - Level2_S |  0.0767201| 0.0156317| 3889.770|  4.907988| 0.0000029|
|Group_3_T - Group_2_L   |level1_O - Level2_S |  0.0156633| 0.0148981| 3889.107|  1.051362| 0.5444832|
|Group_1_H - Group_2_L   |level1_O - Level2_S | -0.0610568| 0.0163169| 3898.048| -3.741947| 0.0005430|

#### Questions

I have some questions based on this. 

1. Is the Anova() function appropriate here? From what I have read online, people use the Anova function to compare models to find out whether the addition of a variable of interest significantly improves the model fit (e.g. https://stats.stackexchange.com/questions/500722/model-interpretation-in-r-anova-vs-summary-output). Can I also use it just to summarise the model I have specified and interpret the results? 

2. Why are the p values for the interaction term (highlighted in both screenshots) different between the summary and Anova outputs? I have been trying to read about this online, and from what I understand the Anova is an omnibus test (i.e. compares several parameters at once) whereas the summary is step-wise. However, I would still expect there to be a significant result for 'Group_VariableGroup_1_H:Variable_CLevel2_S' in the summary as this is (I think?) essentially the same comparison as group 3 vs group 1 in the post hoc comparison table.

If anyone could give me any advice I would hugely appreciate it as, based on what I have here, I currently do not know how to interpret my interaction.

Many thanks in advance.


  [1]: https://i.sstatic.net/3HS9n.png
  [2]: https://i.sstatic.net/AQnOr.png