[R-sig-ME] Interpreting GLMM output and is this the right model?

[Gabriella Kountourides]

Hi everyone,

I emailed a few weeks ago, but am still struggling with this data.
The description of the question below, and model/code/output at the bottom. Many thanks for reading.


I want to look at whether there is a relationship between the way a question is asked (positive, negative, neutral wording) and the sentiment of the response. I have 2638 people asked a question about symptoms. 1/3 of the people were asked it with a negative wording, 1/3 with a neutral one, 1/3 with a positive one. From this, I did sentiment analysis (using Trincker's package) to see whether their responses were more positive or negative, depending on the wording of the question.
Sentiment analysis breaks down responses into sentences, so I have 2638 people, but 7924 sentences, so I would assume to fit ID as a random effect.

The big question is: does the way the question is asked (primetype) affect the polarity/sentiment of the response?
My data is negatively skewed, and has a lot of 0s (this is because some people felt 'neutral'  and so they scored '0'.

Model using the dataframe DF, to see how primetype (this is the way the question is asked) predicts sentiment (the polarity score, which is negatively skewed with lots of 0s), fixed effect is age, and random effect is ID

```
glmmTMB(sentiment ~ primetype + age + (1|id), data=DF)
```


Output:

```
Family: gaussian  ( identity )
Formula:          sentiment ~ primetype + age + (1 | id)
Data: DF

     AIC      BIC                 logLik        deviance       df.resid
  7254.9   7296.5          -3621.4        7242.9         7556

Random effects:

Conditional model:
 Groups          Name                Variance           Std.Dev.
 id                   (Intercept)            8.732e-11     9.344e-06
 Residual                                   1.526e-01         3.906e-01
Number of obs: 7562, groups:  id, 2520

Dispersion estimate for gaussian family (sigma^2): 0.153

Conditional model:
                         Estimate           Std. Error            z value           Pr(>|z|)
(Intercept).   -0.1655972         0.0204310         -8.105           5.27e-16 ***
primetype2   0.0907564         0.0114045          7.958           1.75e-15 ***
primetype3   0.0977533         0.0115802         8.441           < 2e-16 ***
age                -0.0020644          0.0006483       -3.184           0.00145 **
---
Signif. codes:  0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
>
```
How can I interpret whether the model is a good one for my data, is there something else I should be doing? I'm not sure how to interpret the output at all. Would be immensely grateful for any insight


Thanks all


Gabriella Kountourides

DPhil Student | Department of Anthropology

Evolutionary Medicine and Public Health Group

St. John�s College, University of Oxford

gabriella.kountourides using sjc.ox.ac.uk

Tweet me: https://twitter.com/GKountourides

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